feat(route): add query keyword parse of cool paper

[Muyun99]

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Example for the Proposed Route(s) / 路由地址示例

/papers/query/Detection

New RSS Route Checklist / 新 RSS 路由检查表

• New Route / 新的路由
  • Follows Script Standard / 跟随 路由规范
• Anti-bot or rate limit / 反爬/频率限制
  • If yes, do your code reflect this sign? / 如果有, 是否有对应的措施?
• Date and time / 日期和时间
  • Parsed / 可以解析
  • Correct time zone / 时区正确
• New package added / 添加了新的包
• Puppeteer

Note / 说明

增加了 cool paper RSS 源对 query keyword的解析，可以传入一个 keyword，然后自动解析

[github-actions]

Successfully generated as following:

http://localhost:1200/papers/query/Detection - Success ✔️

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    <title>detection</title>
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    <item>
      <title>Facade: High-Precision Insider Threat Detection Using Deep Contextual Anomaly Detection</title>
      <description>&lt;a href=&quot;https://papers.cool/arxiv/2412.06700&quot;&gt;[Site]&lt;/a&gt; &lt;a href=&quot;https://papers.cool/query/2412.06700&quot;&gt;[Kimi]&lt;/a&gt; &lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Alex Kantchelian&lt;/p&gt; &lt;p&gt;We present Facade (Fast and Accurate Contextual Anomaly DEtection): a high-precision deep-learning-based anomaly detection system deployed at Google (a large technology company) as the last line of defense against insider threats since 2018. Facade is an innovative unsupervised action-context system that detects suspicious actions by considering the context surrounding each action, including relevant facts about the user and other entities involved. It is built around a new multi-modal model that is trained on corporate document access, SQL query, and HTTP/RPC request logs. To overcome the scarcity of incident data, Facade harnesses a novel contrastive learning strategy that relies solely on benign data. Its use of history and implicit social network featurization efficiently handles the frequent out-of-distribution events that occur in a rapidly changing corporate environment, and sustains Facade&#39;s high precision performance for a full year after training. Beyond the core model, Facade contributes an innovative clustering approach based on user and action embeddings to improve detection robustness and achieve high precision, multi-scale detection. Functionally what sets Facade apart from existing anomaly detection systems is its high precision. It detects insider attackers with an extremely low false positive rate, lower than 0.01%. For single rogue actions, such as the illegitimate access to a sensitive document, the false positive rate is as low as 0.0003%. To the best of our knowledge, Facade is the only published insider risk anomaly detection system that helps secure such a large corporate environment.&lt;/p&gt; </description>
      <link>https://papers.cool/arxiv/2412.06700</link>
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      <pubDate>Mon, 09 Dec 2024 17:46:28 GMT</pubDate>
      <author>Alex Kantchelian</author>
    </item>
    <item>
      <title>Practitioners&#39; Expectations on Log Anomaly Detection</title>
      <description>&lt;a href=&quot;https://papers.cool/arxiv/2412.01066&quot;&gt;[Site]&lt;/a&gt; &lt;a href=&quot;https://papers.cool/query/2412.01066&quot;&gt;[Kimi]&lt;/a&gt; &lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Xiaoxue Ma&lt;/p&gt; &lt;p&gt;Log anomaly detection has become a common practice for software engineers to analyze software system behavior. Despite significant research efforts in log anomaly detection over the past decade, it remains unclear what are practitioners&#39; expectations on log anomaly detection and whether current research meets their needs. To fill this gap, we conduct an empirical study, surveying 312 practitioners from 36 countries about their expectations on log anomaly detection. In particular, we investigate various factors influencing practitioners&#39; willingness to adopt log anomaly detection tools. We then perform a literature review on log anomaly detection, focusing on publications in premier venues from 2014 to 2024, to compare practitioners&#39; needs with the current state of research. Based on this comparison, we highlight the directions for researchers to focus on to develop log anomaly detection techniques that better meet practitioners&#39; expectations.&lt;/p&gt; </description>
      <link>https://papers.cool/arxiv/2412.01066</link>
      <guid isPermaLink="false">https://papers.cool/arxiv/2412.01066</guid>
      <pubDate>Mon, 02 Dec 2024 03:01:35 GMT</pubDate>
      <author>Xiaoxue Ma</author>
    </item>
    <item>
      <title>From Audio Deepfake Detection to AI-Generated Music Detection -- A Pathway and Overview</title>
      <description>&lt;a href=&quot;https://papers.cool/arxiv/2412.00571&quot;&gt;[Site]&lt;/a&gt; &lt;a href=&quot;https://papers.cool/query/2412.00571&quot;&gt;[Kimi]&lt;/a&gt; &lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Yupei Li&lt;/p&gt; &lt;p&gt;As Artificial Intelligence (AI) technologies continue to evolve, their use in generating realistic, contextually appropriate content has expanded into various domains. Music, an art form and medium for entertainment, deeply rooted into human culture, is seeing an increased involvement of AI into its production. However, the unregulated use of AI music generation (AIGM) tools raises concerns about potential negative impacts on the music industry, copyright and artistic integrity, underscoring the importance of effective AIGM detection. This paper provides an overview of existing AIGM detection methods. To lay a foundation to the general workings and challenges of AIGM detection, we first review general principles of AIGM, including recent advancements in deepfake audios, as well as multimodal detection techniques. We further propose a potential pathway for leveraging foundation models from audio deepfake detection to AIGM detection. Additionally, we discuss implications of these tools and propose directions for future research to address ongoing challenges in the field.&lt;/p&gt; </description>
      <link>https://papers.cool/arxiv/2412.00571</link>
      <guid isPermaLink="false">https://papers.cool/arxiv/2412.00571</guid>
      <pubDate>Sat, 30 Nov 2024 19:53:23 GMT</pubDate>
      <author>Yupei Li</author>
    </item>
    <item>
      <title>Cutting-Edge Detection of Fatigue in Drivers: A Comparative Study of Object Detection Models</title>
      <description>&lt;a href=&quot;https://papers.cool/arxiv/2410.15030&quot;&gt;[Site]&lt;/a&gt; &lt;a href=&quot;https://papers.cool/query/2410.15030&quot;&gt;[Kimi]&lt;/a&gt; &lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Amelia Jones&lt;/p&gt; &lt;p&gt;This research delves into the development of a fatigue detection system based on modern object detection algorithms, particularly YOLO (You Only Look Once) models, including YOLOv5, YOLOv6, YOLOv7, and YOLOv8. By comparing the performance of these models, we evaluate their effectiveness in real-time detection of fatigue-related behavior in drivers. The study addresses challenges like environmental variability and detection accuracy and suggests a roadmap for enhancing real-time detection. Experimental results demonstrate that YOLOv8 offers superior performance, balancing accuracy with speed. Data augmentation techniques and model optimization have been key in enhancing system adaptability to various driving conditions.&lt;/p&gt; </description>
      <link>https://papers.cool/arxiv/2410.15030</link>
      <guid isPermaLink="false">https://papers.cool/arxiv/2410.15030</guid>
      <pubDate>Sat, 19 Oct 2024 08:06:43 GMT</pubDate>
      <author>Amelia Jones</author>
    </item>
    <item>
      <title>Spatiotemporal Object Detection for Improved Aerial Vehicle Detection in Traffic Monitoring</title>
      <description>&lt;a href=&quot;https://papers.cool/arxiv/2410.13616&quot;&gt;[Site]&lt;/a&gt; &lt;a href=&quot;https://papers.cool/query/2410.13616&quot;&gt;[Kimi]&lt;/a&gt; &lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Kristina Telegraph&lt;/p&gt; &lt;p&gt;This work presents advancements in multi-class vehicle detection using UAV cameras through the development of spatiotemporal object detection models. The study introduces a Spatio-Temporal Vehicle Detection Dataset (STVD) containing 6, 600 annotated sequential frame images captured by UAVs, enabling comprehensive training and evaluation of algorithms for holistic spatiotemporal perception. A YOLO-based object detection algorithm is enhanced to incorporate temporal dynamics, resulting in improved performance over single frame models. The integration of attention mechanisms into spatiotemporal models is shown to further enhance performance. Experimental validation demonstrates significant progress, with the best spatiotemporal model exhibiting a 16.22% improvement over single frame models, while it is demonstrated that attention mechanisms hold the potential for additional performance gains.&lt;/p&gt; </description>
      <link>https://papers.cool/arxiv/2410.13616</link>
      <guid isPermaLink="false">https://papers.cool/arxiv/2410.13616</guid>
      <pubDate>Thu, 17 Oct 2024 14:49:37 GMT</pubDate>
      <author>Kristina Telegraph</author>
    </item>
    <item>
      <title>Real-time Fuel Leakage Detection via Online Change Point Detection</title>
      <description>&lt;a href=&quot;https://papers.cool/arxiv/2410.09741&quot;&gt;[Site]&lt;/a&gt; &lt;a href=&quot;https://papers.cool/query/2410.09741&quot;&gt;[Kimi]&lt;/a&gt; &lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Ruimin Chu&lt;/p&gt; &lt;p&gt;Early detection of fuel leakage at service stations with underground petroleum storage systems is a crucial task to prevent catastrophic hazards. Current data-driven fuel leakage detection methods employ offline statistical inventory reconciliation, leading to significant detection delays. Consequently, this can result in substantial financial loss and environmental impact on the surrounding community. In this paper, we propose a novel framework called Memory-based Online Change Point Detection (MOCPD) which operates in near real-time, enabling early detection of fuel leakage. MOCPD maintains a collection of representative historical data within a size-constrained memory, along with an adaptively computed threshold. Leaks are detected when the dissimilarity between the latest data and historical memory exceeds the current threshold. An update phase is incorporated in MOCPD to ensure diversity among historical samples in the memory. With this design, MOCPD is more robust and achieves a better recall rate while maintaining a reasonable precision score. We have conducted a variety of experiments comparing MOCPD to commonly used online change point detection (CPD) baselines on real-world fuel variance data with induced leakages, actual fuel leakage data and benchmark CPD datasets. Overall, MOCPD consistently outperforms the baseline methods in terms of detection accuracy, demonstrating its applicability to fuel leakage detection and CPD problems.&lt;/p&gt; </description>
      <link>https://papers.cool/arxiv/2410.09741</link>
      <guid isPermaLink="false">https://papers.cool/arxiv/2410.09741</guid>
      <pubDate>Sun, 13 Oct 2024 06:22:13 GMT</pubDate>
      <author>Ruimin Chu</author>
    </item>
    <item>
      <title>1M-Deepfakes Detection Challenge</title>
      <description>&lt;a href=&quot;https://papers.cool/arxiv/2409.06991&quot;&gt;[Site]&lt;/a&gt; &lt;a href=&quot;https://papers.cool/query/2409.06991&quot;&gt;[Kimi]&lt;/a&gt; &lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Zhixi Cai&lt;/p&gt; &lt;p&gt;The detection and localization of deepfake content, particularly when small fake segments are seamlessly mixed with real videos, remains a significant challenge in the field of digital media security. Based on the recently released AV-Deepfake1M dataset, which contains more than 1 million manipulated videos across more than 2,000 subjects, we introduce the 1M-Deepfakes Detection Challenge. This challenge is designed to engage the research community in developing advanced methods for detecting and localizing deepfake manipulations within the large-scale high-realistic audio-visual dataset. The participants can access the AV-Deepfake1M dataset and are required to submit their inference results for evaluation across the metrics for detection or localization tasks. The methodologies developed through the challenge will contribute to the development of next-generation deepfake detection and localization systems. Evaluation scripts, baseline models, and accompanying code will be available on https://github.com/ControlNet/AV-Deepfake1M.&lt;/p&gt; </description>
      <link>https://papers.cool/arxiv/2409.06991</link>
      <guid isPermaLink="false">https://papers.cool/arxiv/2409.06991</guid>
      <pubDate>Wed, 11 Sep 2024 03:43:53 GMT</pubDate>
      <author>Zhixi Cai</author>
    </item>
    <item>
      <title>Active-IRS-Enabled Target Detection</title>
      <description>&lt;a href=&quot;https://papers.cool/arxiv/2409.04155&quot;&gt;[Site]&lt;/a&gt; &lt;a href=&quot;https://papers.cool/query/2409.04155&quot;&gt;[Kimi]&lt;/a&gt; &lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Song Xianxin&lt;/p&gt; &lt;p&gt;This letter studies an active intelligent reflecting surface (IRS)-enabled non-line-of-sight (NLoS) target detection system, in which an active IRS equipped with active reflecting elements and sensors is strategically deployed to facilitate target detection in the NLoS region of the base station (BS) by processing echo signals through the BS-IRS-target-IRS link. First, we design an optimal detector based on the Neyman-Pearson (NP) theorem and derive the corresponding detection probability. Intriguingly, it is demonstrated that the optimal detector can exploit both the BS&#39;s transmit signal and the active IRS&#39;s reflection noise for more effective detection. Subsequently, we jointly optimize the transmit beamforming at the BS and the reflective beamforming at the active IRS to maximize the detection probability, subject to the maximum transmit power constraint at the BS, as well as the maximum amplification power and gain constraints at the active IRS. Finally, simulation results unveil that the proposed joint beamforming design significantly enhances the detection probability, with the active IRS outperforming its fully- and semi-passive counterparts in detection performance.&lt;/p&gt; </description>
      <link>https://papers.cool/arxiv/2409.04155</link>
      <guid isPermaLink="false">https://papers.cool/arxiv/2409.04155</guid>
      <pubDate>Fri, 06 Sep 2024 09:34:55 GMT</pubDate>
      <author>Song Xianxin</author>
    </item>
    <item>
      <title>BFA-YOLO: Balanced multiscale object detection network for multi-view building facade attachments detection</title>
      <description>&lt;a href=&quot;https://papers.cool/arxiv/2409.04025&quot;&gt;[Site]&lt;/a&gt; &lt;a href=&quot;https://papers.cool/query/2409.04025&quot;&gt;[Kimi]&lt;/a&gt; &lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Yangguang Chen&lt;/p&gt; &lt;p&gt;Detection of building facade attachments such as doors, windows, balconies, air conditioner units, billboards, and glass curtain walls plays a pivotal role in numerous applications. Building facade attachments detection aids in vbuilding information modeling (BIM) construction and meeting Level of Detail 3 (LOD3) standards. Yet, it faces challenges like uneven object distribution, small object detection difficulty, and background interference. To counter these, we propose BFA-YOLO, a model for detecting facade attachments in multi-view images. BFA-YOLO incorporates three novel innovations: the Feature Balanced Spindle Module (FBSM) for addressing uneven distribution, the Target Dynamic Alignment Task Detection Head (TDATH) aimed at improving small object detection, and the Position Memory Enhanced Self-Attention Mechanism (PMESA) to combat background interference, with each component specifically designed to solve its corresponding challenge. Detection efficacy of deep network models deeply depends on the dataset&#39;s characteristics. Existing open source datasets related to building facades are limited by their single perspective, small image pool, and incomplete category coverage. We propose a novel method for building facade attachments detection dataset construction and construct the BFA-3D dataset for facade attachments detection. The BFA-3D dataset features multi-view, accurate labels, diverse categories, and detailed classification. BFA-YOLO surpasses YOLOv8 by 1.8% and 2.9% in [email protected] on the multi-view BFA-3D and street-view Facade-WHU datasets, respectively. These results underscore BFA-YOLO&#39;s superior performance in detecting facade attachments.&lt;/p&gt; </description>
      <link>https://papers.cool/arxiv/2409.04025</link>
      <guid isPermaLink="false">https://papers.cool/arxiv/2409.04025</guid>
      <pubDate>Fri, 06 Sep 2024 04:44:52 GMT</pubDate>
      <author>Yangguang Chen</author>
    </item>
    <item>
      <title>Missile detection and destruction robot using detection algorithm</title>
      <description>&lt;a href=&quot;https://papers.cool/arxiv/2407.07452&quot;&gt;[Site]&lt;/a&gt; &lt;a href=&quot;https://papers.cool/query/2407.07452&quot;&gt;[Kimi]&lt;/a&gt; &lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Md Kamrul Siam&lt;/p&gt; &lt;p&gt;This research is based on the present missile detection technologies in the world and the analysis of these technologies to find a cost effective solution to implement the system in Bangladesh. The paper will give an idea of the missile detection technologies using the electro-optical sensor and the pulse doppler radar. The system is made to detect the target missile. Automatic detection and destruction with the help of ultrasonic sonar, a metal detector sensor, and a smoke detector sensor. The system is mainly based on an ultrasonic sonar sensor. It has a transducer, a transmitter, and a receiver. Transducer is connected with the connected with controller. When it detects an object by following the algorithm, it finds its distance and angle. It can also assure whether the system can destroy the object or not by using another algorithm&#39;s simulation.&lt;/p&gt; </description>
      <link>https://papers.cool/arxiv/2407.07452</link>
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      <pubDate>Wed, 10 Jul 2024 08:12:21 GMT</pubDate>
      <author>Md Kamrul Siam</author>
    </item>
    <item>
      <title>Small Aerial Target Detection for Airborne Infrared Detection Systems using LightGBM and Trajectory Constraints</title>
      <description>&lt;a href=&quot;https://papers.cool/arxiv/2407.01278&quot;&gt;[Site]&lt;/a&gt; &lt;a href=&quot;https://papers.cool/query/2407.01278&quot;&gt;[Kimi]&lt;/a&gt; &lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Xiaoliang Sun&lt;/p&gt; &lt;p&gt;Factors, such as rapid relative motion, clutter background, etc., make robust small aerial target detection for airborne infrared detection systems a challenge. Existing methods are facing difficulties when dealing with such cases. We consider that a continuous and smooth trajectory is critical in boosting small infrared aerial target detection performance. A simple and effective small aerial target detection method for airborne infrared detection system using light gradient boosting model (LightGBM) and trajectory constraints is proposed in this article. First, we simply formulate target candidate detection as a binary classification problem. Target candidates in every individual frame are detected via interesting pixel detection and a trained LightGBM model. Then, the local smoothness and global continuous characteristic of the target trajectory are modeled as short-strict and long-loose constraints. The trajectory constraints are used efficiently for detecting the true small infrared aerial targets from numerous target candidates. Experiments on public datasets demonstrate that the proposed method performs better than other existing methods. Furthermore, a public dataset for small aerial target detection in airborne infrared detection systems is constructed. To the best of our knowledge, this dataset has the largest data scale and richest scene types within this field.&lt;/p&gt; </description>
      <link>https://papers.cool/arxiv/2407.01278</link>
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      <pubDate>Mon, 01 Jul 2024 13:33:40 GMT</pubDate>
      <author>Xiaoliang Sun</author>
    </item>
    <item>
      <title>Language-driven Grasp Detection</title>
      <description>&lt;a href=&quot;https://papers.cool/arxiv/2406.09489&quot;&gt;[Site]&lt;/a&gt; &lt;a href=&quot;https://papers.cool/query/2406.09489&quot;&gt;[Kimi]&lt;/a&gt; &lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; An Dinh Vuong&lt;/p&gt; &lt;p&gt;Grasp detection is a persistent and intricate challenge with various industrial applications. Recently, many methods and datasets have been proposed to tackle the grasp detection problem. However, most of them do not consider using natural language as a condition to detect the grasp poses. In this paper, we introduce Grasp-Anything++, a new language-driven grasp detection dataset featuring 1M samples, over 3M objects, and upwards of 10M grasping instructions. We utilize foundation models to create a large-scale scene corpus with corresponding images and grasp prompts. We approach the language-driven grasp detection task as a conditional generation problem. Drawing on the success of diffusion models in generative tasks and given that language plays a vital role in this task, we propose a new language-driven grasp detection method based on diffusion models. Our key contribution is the contrastive training objective, which explicitly contributes to the denoising process to detect the grasp pose given the language instructions. We illustrate that our approach is theoretically supportive. The intensive experiments show that our method outperforms state-of-the-art approaches and allows real-world robotic grasping. Finally, we demonstrate our large-scale dataset enables zero-short grasp detection and is a challenging benchmark for future work. Project website: https://airvlab.github.io/grasp-anything/&lt;/p&gt; </description>
      <link>https://papers.cool/arxiv/2406.09489</link>
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      <pubDate>Thu, 13 Jun 2024 16:06:59 GMT</pubDate>
      <author>An Dinh Vuong</author>
    </item>
    <item>
      <title>Detection-Rate-Emphasized Multi-objective Evolutionary Feature Selection for Network Intrusion Detection</title>
      <description>&lt;a href=&quot;https://papers.cool/arxiv/2406.09180&quot;&gt;[Site]&lt;/a&gt; &lt;a href=&quot;https://papers.cool/query/2406.09180&quot;&gt;[Kimi]&lt;/a&gt; &lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Zi-Hang Cheng&lt;/p&gt; &lt;p&gt;Network intrusion detection is one of the most important issues in the field of cyber security, and various machine learning techniques have been applied to build intrusion detection systems. However, since the number of features to describe the network connections is often large, where some features are redundant or noisy, feature selection is necessary in such scenarios, which can both improve the efficiency and accuracy. Recently, some researchers focus on using multi-objective evolutionary algorithms (MOEAs) to select features. But usually, they only consider the number of features and classification accuracy as the objectives, resulting in unsatisfactory performance on a critical metric, detection rate. This will lead to the missing of many real attacks and bring huge losses to the network system. In this paper, we propose DR-MOFS to model the feature selection problem in network intrusion detection as a three-objective optimization problem, where the number of features, accuracy and detection rate are optimized simultaneously, and use MOEAs to solve it. Experiments on two popular network intrusion detection datasets NSL-KDD and UNSW-NB15 show that in most cases the proposed method can outperform previous methods, i.e., lead to fewer features, higher accuracy and detection rate.&lt;/p&gt; </description>
      <link>https://papers.cool/arxiv/2406.09180</link>
      <guid isPermaLink="false">https://papers.cool/arxiv/2406.09180</guid>
      <pubDate>Thu, 13 Jun 2024 14:42:17 GMT</pubDate>
      <author>Zi-Hang Cheng</author>
    </item>
    <item>
      <title>Tracking Small Birds by Detection Candidate Region Filtering and Detection History-aware Association</title>
      <description>&lt;a href=&quot;https://papers.cool/arxiv/2405.17323&quot;&gt;[Site]&lt;/a&gt; &lt;a href=&quot;https://papers.cool/query/2405.17323&quot;&gt;[Kimi]&lt;/a&gt; &lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Tingwei Liu&lt;/p&gt; &lt;p&gt;This paper focuses on tracking birds that appear small in a panoramic video. When the size of the tracked object is small in the image (small object tracking) and move quickly, object detection and association suffers. To address these problems, we propose Adaptive Slicing Aided Hyper Inference (Adaptive SAHI), which reduces the candidate regions to apply detection, and Detection History-aware Similarity Criterion (DHSC), which accurately associates objects in consecutive frames based on the detection history. Experiments on the NUBird2022 dataset verifies the effectiveness of the proposed method by showing improvements in both accuracy and speed.&lt;/p&gt; </description>
      <link>https://papers.cool/arxiv/2405.17323</link>
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      <pubDate>Mon, 27 May 2024 16:22:38 GMT</pubDate>
      <author>Tingwei Liu</author>
    </item>
    <item>
      <title>Quantum Edge Detection</title>
      <description>&lt;a href=&quot;https://papers.cool/arxiv/2405.11373&quot;&gt;[Site]&lt;/a&gt; &lt;a href=&quot;https://papers.cool/query/2405.11373&quot;&gt;[Kimi]&lt;/a&gt; &lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Santiago Llorens&lt;/p&gt; &lt;p&gt;This paper introduces quantum edge detection, aimed at locating boundaries of quantum domains where all particles share the same pure state. Focusing on the 1D scenario of a string of particles, we develop an optimal protocol for quantum edge detection, efficiently computing its success probability through Schur-Weyl duality and semidefinite programming techniques. We analyze the behavior of the success probability as a function of the string length and local dimension, with emphasis in the limit of long strings. We present a protocol based on square root measurement, which proves asymptotically optimal. Additionally, we explore a mixed quantum change point detection scenario where the state of particles transitions from known to unknown, which may find practical applications in detecting malfunctions in quantum devices&lt;/p&gt; </description>
      <link>https://papers.cool/arxiv/2405.11373</link>
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      <pubDate>Sat, 18 May 2024 19:22:15 GMT</pubDate>
      <author>Santiago Llorens</author>
    </item>
    <item>
      <title>Roadside Monocular 3D Detection via 2D Detection Prompting</title>
      <description>&lt;a href=&quot;https://papers.cool/arxiv/2404.01064&quot;&gt;[Site]&lt;/a&gt; &lt;a href=&quot;https://papers.cool/query/2404.01064&quot;&gt;[Kimi]&lt;/a&gt; &lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Yechi Ma&lt;/p&gt; &lt;p&gt;The problem of roadside monocular 3D detection requires detecting objects of interested classes in a 2D RGB frame and predicting their 3D information such as locations in bird&#39;s-eye-view (BEV). It has broad applications in traffic control, vehicle-vehicle communication, and vehicle-infrastructure cooperative perception. To approach this problem, we present a novel and simple method by prompting the 3D detector using 2D detections. Our method builds on a key insight that, compared with 3D detectors, a 2D detector is much easier to train and performs significantly better w.r.t detections on the 2D image plane. That said, one can exploit 2D detections of a well-trained 2D detector as prompts to a 3D detector, being trained in a way of inflating such 2D detections to 3D towards 3D detection. To construct better prompts using the 2D detector, we explore three techniques: (a) concatenating both 2D and 3D detectors&#39; features, (b) attentively fusing 2D and 3D detectors&#39; features, and (c) encoding predicted 2D boxes x, y, width, height, label and attentively fusing such with the 3D detector&#39;s features. Surprisingly, the third performs the best. Moreover, we present a yaw tuning tactic and a class-grouping strategy that merges classes based on their functionality; these techniques improve 3D detection performance further. Comprehensive ablation studies and extensive experiments demonstrate that our method resoundingly outperforms prior works, achieving the state-of-the-art on two large-scale roadside 3D detection benchmarks.&lt;/p&gt; </description>
      <link>https://papers.cool/arxiv/2404.01064</link>
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      <pubDate>Mon, 01 Apr 2024 11:57:34 GMT</pubDate>
      <author>Yechi Ma</author>
    </item>
    <item>
      <title>BAM: Box Abstraction Monitors for Real-time OoD Detection in Object Detection</title>
      <description>&lt;a href=&quot;https://papers.cool/arxiv/2403.18373&quot;&gt;[Site]&lt;/a&gt; &lt;a href=&quot;https://papers.cool/query/2403.18373&quot;&gt;[Kimi]&lt;/a&gt; &lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Changshun Wu&lt;/p&gt; &lt;p&gt;Out-of-distribution (OoD) detection techniques for deep neural networks (DNNs) become crucial thanks to their filtering of abnormal inputs, especially when DNNs are used in safety-critical applications and interact with an open and dynamic environment. Nevertheless, integrating OoD detection into state-of-the-art (SOTA) object detection DNNs poses significant challenges, partly due to the complexity introduced by the SOTA OoD construction methods, which require the modification of DNN architecture and the introduction of complex loss functions. This paper proposes a simple, yet surprisingly effective, method that requires neither retraining nor architectural change in object detection DNN, called Box Abstraction-based Monitors (BAM). The novelty of BAM stems from using a finite union of convex box abstractions to capture the learned features of objects for in-distribution (ID) data, and an important observation that features from OoD data are more likely to fall outside of these boxes. The union of convex regions within the feature space allows the formation of non-convex and interpretable decision boundaries, overcoming the limitations of VOS-like detectors without sacrificing real-time performance. Experiments integrating BAM into Faster R-CNN-based object detection DNNs demonstrate a considerably improved performance against SOTA OoD detection techniques.&lt;/p&gt; </description>
      <link>https://papers.cool/arxiv/2403.18373</link>
      <guid isPermaLink="false">https://papers.cool/arxiv/2403.18373</guid>
      <pubDate>Wed, 27 Mar 2024 09:10:01 GMT</pubDate>
      <author>Changshun Wu</author>
    </item>
    <item>
      <title>GPT-generated Text Detection: Benchmark Dataset and Tensor-based Detection Method</title>
      <description>&lt;a href=&quot;https://papers.cool/arxiv/2403.07321&quot;&gt;[Site]&lt;/a&gt; &lt;a href=&quot;https://papers.cool/query/2403.07321&quot;&gt;[Kimi]&lt;/a&gt; &lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Zubair Qazi&lt;/p&gt; &lt;p&gt;As natural language models like ChatGPT become increasingly prevalent in applications and services, the need for robust and accurate methods to detect their output is of paramount importance. In this paper, we present GPT Reddit Dataset (GRiD), a novel Generative Pretrained Transformer (GPT)-generated text detection dataset designed to assess the performance of detection models in identifying generated responses from ChatGPT. The dataset consists of a diverse collection of context-prompt pairs based on Reddit, with human-generated and ChatGPT-generated responses. We provide an analysis of the dataset&#39;s characteristics, including linguistic diversity, context complexity, and response quality. To showcase the dataset&#39;s utility, we benchmark several detection methods on it, demonstrating their efficacy in distinguishing between human and ChatGPT-generated responses. This dataset serves as a resource for evaluating and advancing detection techniques in the context of ChatGPT and contributes to the ongoing efforts to ensure responsible and trustworthy AI-driven communication on the internet. Finally, we propose GpTen, a novel tensor-based GPT text detection method that is semi-supervised in nature since it only has access to human-generated text and performs on par with fully-supervised baselines.&lt;/p&gt; </description>
      <link>https://papers.cool/arxiv/2403.07321</link>
      <guid isPermaLink="false">https://papers.cool/arxiv/2403.07321</guid>
      <pubDate>Tue, 12 Mar 2024 05:15:21 GMT</pubDate>
      <author>Zubair Qazi</author>
    </item>
    <item>
      <title>FriendNet: Detection-Friendly Dehazing Network</title>
      <description>&lt;a href=&quot;https://papers.cool/arxiv/2403.04443&quot;&gt;[Site]&lt;/a&gt; &lt;a href=&quot;https://papers.cool/query/2403.04443&quot;&gt;[Kimi]&lt;/a&gt; &lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Yihua Fan&lt;/p&gt; &lt;p&gt;Adverse weather conditions often impair the quality of captured images, inevitably inducing cutting-edge object detection models for advanced driver assistance systems (ADAS) and autonomous driving. In this paper, we raise an intriguing question: can the combination of image restoration and object detection enhance detection performance in adverse weather conditions? To answer it, we propose an effective architecture that bridges image dehazing and object detection together via guidance information and task-driven learning to achieve detection-friendly dehazing, termed FriendNet. FriendNet aims to deliver both high-quality perception and high detection capacity. Different from existing efforts that intuitively treat image dehazing as pre-processing, FriendNet establishes a positive correlation between these two tasks. Clean features generated by the dehazing network potentially contribute to improvements in object detection performance. Conversely, object detection crucially guides the learning process of the image dehazing network under the task-driven learning scheme. We shed light on how downstream tasks can guide upstream dehazing processes, considering both network architecture and learning objectives. We design Guidance Fusion Block (GFB) and Guidance Attention Block (GAB) to facilitate the integration of detection information into the network. Furthermore, the incorporation of the detection task loss aids in refining the optimization process. Additionally, we introduce a new Physics-aware Feature Enhancement Block (PFEB), which integrates physics-based priors to enhance the feature extraction and representation capabilities. Extensive experiments on synthetic and real-world datasets demonstrate the superiority of our method over state-of-the-art methods on both image quality and detection precision. Our source code is available at https://github.com/fanyihua0309/FriendNet.&lt;/p&gt; </description>
      <link>https://papers.cool/arxiv/2403.04443</link>
      <guid isPermaLink="false">https://papers.cool/arxiv/2403.04443</guid>
      <pubDate>Thu, 07 Mar 2024 12:19:04 GMT</pubDate>
      <author>Yihua Fan</author>
    </item>
    <item>
      <title>Order-detection, representation-detection, and applications to cable knots</title>
      <description>&lt;a href=&quot;https://papers.cool/arxiv/2402.15465&quot;&gt;[Site]&lt;/a&gt; &lt;a href=&quot;https://papers.cool/query/2402.15465&quot;&gt;[Kimi]&lt;/a&gt; &lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Adam Clay&lt;/p&gt; &lt;p&gt;Given a $3$-manifold $M$ with multiple incompressible torus boundary components, we develop a general definition of order-detection of tuples of slopes on the boundary components of $M$. In parallel, we arrive at a general definition of representation-detection of tuples of slopes, and show that these two kinds of slope detection are equivalent -- in the sense that a tuple of slopes on the boundary of $M$ is order-detected if and only if it is representation-detected. We use these results, together with new &quot;relative gluing theorems,&quot; to show how the work of Eisenbud-Hirsch-Neumann, Jankins-Neumann and Naimi can be used to determine tuples of representation-detected slopes and, in turn, the behaviour of order-detected slopes on the boundary of a knot manifold with respect to cabling. Our cabling results improve upon work of the first author and Watson, and in particular, this new approach shows how one can use the equivalence between representation-detection and order-detection to derive orderability results that parallel known behaviour of L-spaces with respect to Dehn filling.&lt;/p&gt; </description>
      <link>https://papers.cool/arxiv/2402.15465</link>
      <guid isPermaLink="false">https://papers.cool/arxiv/2402.15465</guid>
      <pubDate>Fri, 23 Feb 2024 17:55:28 GMT</pubDate>
      <author>Adam Clay</author>
    </item>
    <item>
      <title>Optimal non-Gaussian operations in difference-intensity detection and parity detection-based Mach-Zehnder interferometer</title>
      <description>&lt;a href=&quot;https://papers.cool/arxiv/2312.10774&quot;&gt;[Site]&lt;/a&gt; &lt;a href=&quot;https://papers.cool/query/2312.10774&quot;&gt;[Kimi]&lt;/a&gt; &lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Manali Verma&lt;/p&gt; &lt;p&gt;We investigate the benefits of probabilistic non-Gaussian operations in phase estimation using difference-intensity and parity detection-based Mach-Zehnder interferometers (MZI). We consider an experimentally implementable model to perform three different non-Gaussian operations, namely photon subtraction (PS), photon addition (PA), and photon catalysis (PC) on a single-mode squeezed vacuum (SSV) state. In difference-intensity detection-based MZI, two PC operation is found to be the most optimal, while for parity detection-based MZI, two PA operation emerges as the most optimal process. We have also provided the corresponding squeezing and transmissivity parameters at best performance, making our study relevant for experimentalists. Further, we have derived the general expression of moment-generating function, which shall be useful in exploring other detection schemes such as homodyne detection and quadratic homodyne detection.&lt;/p&gt; </description>
      <link>https://papers.cool/arxiv/2312.10774</link>
      <guid isPermaLink="false">https://papers.cool/arxiv/2312.10774</guid>
      <pubDate>Sun, 17 Dec 2023 17:52:14 GMT</pubDate>
      <author>Manali Verma</author>
    </item>
    <item>
      <title>Effects of detection-beam focal offset on displacement detection in optical tweezers</title>
      <description>&lt;a href=&quot;https://papers.cool/arxiv/2311.06088&quot;&gt;[Site]&lt;/a&gt; &lt;a href=&quot;https://papers.cool/query/2311.06088&quot;&gt;[Kimi]&lt;/a&gt; &lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Anni Chen&lt;/p&gt; &lt;p&gt;A high-resolution displacement detection can be achieved by analyzing the scattered light of the trapping beams from the particle in optical tweezers. In some applications where trapping and displacement detection need to be separated, a detection beam can be introduced for independent displacement detection. However, the detection beam focus possibly deviates from the centre of the particle, which will affect the performance of the displacement detection. In this paper, we detect the radial displacement of the particle by utilizing the forward scattered light of the detection beam from the particle. The effects of the lateral and axial offsets between the detection beam focus and the particle centre on the displacement detection are analyzed by the simulation and experiment. The results show that the lateral offsets will decrease the detection sensitivity and linear range and aggravate the crosstalk between the x-direction signal and y-direction signal of QPD. The axial offsets also affect the detection sensitivity, an optimal axial offset can improve the sensitivity of the displacement detection substantially. In addition, the influence of system parameters, such as particle radius a, numerical aperture of the condenser NAc and numerical aperture of the objective NAo on the optimal axial offset are discussed. A combination of conventional optical tweezers instrument and a detection beam provides a more flexible working point, allowing for the active modulation of the sensitivity and linear range of the displacement detection. This work would be of great interest for improving the accuracy of the displacement and force detection performed by the optical tweezers.&lt;/p&gt; </description>
      <link>https://papers.cool/arxiv/2311.06088</link>
      <guid isPermaLink="false">https://papers.cool/arxiv/2311.06088</guid>
      <pubDate>Fri, 10 Nov 2023 14:41:04 GMT</pubDate>
      <author>Anni Chen</author>
    </item>
    <item>
      <title>Post-experiment coincidence detection techniques for direct detection of two-body correlations</title>
      <description>&lt;a href=&quot;https://papers.cool/arxiv/2308.16746&quot;&gt;[Site]&lt;/a&gt; &lt;a href=&quot;https://papers.cool/query/2308.16746&quot;&gt;[Kimi]&lt;/a&gt; &lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Dezhong Cao&lt;/p&gt; &lt;p&gt;It is one challenge to develop experimental techniques for direct detection of the many-body correlations of strongly correlated electrons, which exhibit a variety of unsolved mysteries. In this article, we present a post-experiment coincidence counting method and propose two post-experiment coincidence detection techniques, post-experiment coincidence angle-resolved photoemission spectroscopy (cARPES) and post-experiment coincidence inelastic neutron scattering (cINS). By coincidence detection of two photoelectric processes or two neutron-scattering processes, the post-experiment coincidence detection techniques can detect directly the two-body correlations of strongly correlated electrons in particle-particle channel or two-spin channel. The post-experiment coincidence detection techniques can be implemented upon the pulse-resolved angle-resolved photoemission spectroscopy (ARPES) or inelastic neutron scattering (INS) experimental apparatus with pulse photon or neutron source. When implemented experimentally, they will be powerful techniques to study the highly esoteric high-temperature superconductivity and the highly coveted quantum spin liquids.&lt;/p&gt; </description>
      <link>https://papers.cool/arxiv/2308.16746</link>
      <guid isPermaLink="false">https://papers.cool/arxiv/2308.16746</guid>
      <pubDate>Thu, 31 Aug 2023 14:06:23 GMT</pubDate>
      <author>Dezhong Cao</author>
    </item>
    <item>
      <title>Described Object Detection: Liberating Object Detection with Flexible Expressions</title>
      <description>&lt;a href=&quot;https://papers.cool/arxiv/2307.12813&quot;&gt;[Site]&lt;/a&gt; &lt;a href=&quot;https://papers.cool/query/2307.12813&quot;&gt;[Kimi]&lt;/a&gt; &lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Chi Xie&lt;/p&gt; &lt;p&gt;Detecting objects based on language information is a popular task that includes Open-Vocabulary object Detection (OVD) and Referring Expression Comprehension (REC). In this paper, we advance them to a more practical setting called Described Object Detection (DOD) by expanding category names to flexible language expressions for OVD and overcoming the limitation of REC only grounding the pre-existing object. We establish the research foundation for DOD by constructing a Description Detection Dataset ($D^3$). This dataset features flexible language expressions, whether short category names or long descriptions, and annotating all described objects on all images without omission. By evaluating previous SOTA methods on $D^3$, we find some troublemakers that fail current REC, OVD, and bi-functional methods. REC methods struggle with confidence scores, rejecting negative instances, and multi-target scenarios, while OVD methods face constraints with long and complex descriptions. Recent bi-functional methods also do not work well on DOD due to their separated training procedures and inference strategies for REC and OVD tasks. Building upon the aforementioned findings, we propose a baseline that largely improves REC methods by reconstructing the training data and introducing a binary classification sub-task, outperforming existing methods. Data and code are available at https://github.com/shikras/d-cube and related works are tracked in https://github.com/Charles-Xie/awesome-described-object-detection.&lt;/p&gt; </description>
      <link>https://papers.cool/arxiv/2307.12813</link>
      <guid isPermaLink="false">https://papers.cool/arxiv/2307.12813</guid>
      <pubDate>Mon, 24 Jul 2023 14:06:54 GMT</pubDate>
      <author>Chi Xie</author>
    </item>
    <item>
      <title>Joint Microseismic Event Detection and Location with a Detection Transformer</title>
      <description>&lt;a href=&quot;https://papers.cool/arxiv/2307.09207&quot;&gt;[Site]&lt;/a&gt; &lt;a href=&quot;https://papers.cool/query/2307.09207&quot;&gt;[Kimi]&lt;/a&gt; &lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Yuanyuan Yang&lt;/p&gt; &lt;p&gt;Microseismic event detection and location are two primary components in microseismic monitoring, which offers us invaluable insights into the subsurface during reservoir stimulation and evolution. Conventional approaches for event detection and location often suffer from manual intervention and/or heavy computation, while current machine learning-assisted approaches typically address detection and location separately; such limitations hinder the potential for real-time microseismic monitoring. We propose an approach to unify event detection and source location into a single framework by adapting a Convolutional Neural Network backbone and an encoder-decoder Transformer with a set-based Hungarian loss, which is applied directly to recorded waveforms. The proposed network is trained on synthetic data simulating multiple microseismic events corresponding to random source locations in the area of suspected microseismic activities. A synthetic test on a 2D profile of the SEAM Time Lapse model illustrates the capability of the proposed method in detecting the events properly and locating them in the subsurface accurately; while, a field test using the Arkoma Basin data further proves its practicability, efficiency, and its potential in paving the way for real-time monitoring of microseismic events.&lt;/p&gt; </description>
      <link>https://papers.cool/arxiv/2307.09207</link>
      <guid isPermaLink="false">https://papers.cool/arxiv/2307.09207</guid>
      <pubDate>Sun, 16 Jul 2023 10:56:46 GMT</pubDate>
      <author>Yuanyuan Yang</author>
    </item>
    <item>
      <title>Detection-Recovery and Detection-Refutation Gaps via Reductions from Planted Clique</title>
      <description>&lt;a href=&quot;https://papers.cool/arxiv/2306.17719&quot;&gt;[Site]&lt;/a&gt; &lt;a href=&quot;https://papers.cool/query/2306.17719&quot;&gt;[Kimi]&lt;/a&gt; &lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Guy Bresler&lt;/p&gt; &lt;p&gt;Planted Dense Subgraph (PDS) problem is a prototypical problem with a computational-statistical gap. It also exhibits an intriguing additional phenomenon: different tasks, such as detection or recovery, appear to have different computational limits. A detection-recovery gap for PDS was substantiated in the form of a precise conjecture given by Chen and Xu (2014) (based on the parameter values for which a convexified MLE succeeds) and then shown to hold for low-degree polynomial algorithms by Schramm and Wein (2022) and for MCMC algorithms for Ben Arous et al. (2020). In this paper, we demonstrate that a slight variation of the Planted Clique Hypothesis with secret leakage (introduced in Brennan and Bresler (2020)), implies a detection-recovery gap for PDS. In the same vein, we also obtain a sharp lower bound for refutation, yielding a detection-refutation gap. Our methods build on the framework of Brennan and Bresler (2020) to construct average-case reductions mapping secret leakage Planted Clique to appropriate target problems.&lt;/p&gt; </description>
      <link>https://papers.cool/arxiv/2306.17719</link>
      <guid isPermaLink="false">https://papers.cool/arxiv/2306.17719</guid>
      <pubDate>Fri, 30 Jun 2023 15:02:47 GMT</pubDate>
      <author>Guy Bresler</author>
    </item>
    <item>
      <title>Taming Detection Transformers for Medical Object Detection</title>
      <description>&lt;a href=&quot;https://papers.cool/arxiv/2306.15472&quot;&gt;[Site]&lt;/a&gt; &lt;a href=&quot;https://papers.cool/query/2306.15472&quot;&gt;[Kimi]&lt;/a&gt; &lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Marc K. Ickler&lt;/p&gt; &lt;p&gt;The accurate detection of suspicious regions in medical images is an error-prone and time-consuming process required by many routinely performed diagnostic procedures. To support clinicians during this difficult task, several automated solutions were proposed relying on complex methods with many hyperparameters. In this study, we investigate the feasibility of DEtection TRansformer (DETR) models for volumetric medical object detection. In contrast to previous works, these models directly predict a set of objects without relying on the design of anchors or manual heuristics such as non-maximum-suppression to detect objects. We show by conducting extensive experiments with three models, namely DETR, Conditional DETR, and DINO DETR on four data sets (CADA, RibFrac, KiTS19, and LIDC) that these set prediction models can perform on par with or even better than currently existing methods. DINO DETR, the best-performing model in our experiments demonstrates this by outperforming a strong anchor-based one-stage detector, Retina U-Net, on three out of four data sets.&lt;/p&gt; </description>
      <link>https://papers.cool/arxiv/2306.15472</link>
      <guid isPermaLink="false">https://papers.cool/arxiv/2306.15472</guid>
      <pubDate>Tue, 27 Jun 2023 13:46:15 GMT</pubDate>
      <author>Marc K. Ickler</author>
    </item>
    <item>
      <title>WePaMaDM-Outlier Detection: Weighted Outlier Detection using Pattern Approaches for Mass Data Mining</title>
      <description>&lt;a href=&quot;https://papers.cool/arxiv/2306.06139&quot;&gt;[Site]&lt;/a&gt; &lt;a href=&quot;https://papers.cool/query/2306.06139&quot;&gt;[Kimi]&lt;/a&gt; &lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Ravindrakumar Purohit&lt;/p&gt; &lt;p&gt;Weighted Outlier Detection is a method for identifying unusual or anomalous data points in a dataset, which can be caused by various factors like human error, fraud, or equipment malfunctions. Detecting outliers can reveal vital information about system faults, fraudulent activities, and patterns in the data, assisting experts in addressing the root causes of these anomalies. However,creating a model of normal data patterns to identify outliers can be challenging due to the nature of input data, labeled data availability, and specific requirements of the problem. This article proposed the WePaMaDM-Outlier Detection with distinct mass data mining domain, demonstrating that such techniques are domain-dependent and usually developed for specific problem formulations. Nevertheless, similar domains can adapt solutions with modifications. This work also investigates the significance of data modeling in outlier detection techniques in surveillance, fault detection, and trend analysis, also referred to as novelty detection, a semisupervised task where the algorithm learns to recognize abnormality while being taught the normal class.&lt;/p&gt; </description>
      <link>https://papers.cool/arxiv/2306.06139</link>
      <guid isPermaLink="false">https://papers.cool/arxiv/2306.06139</guid>
      <pubDate>Fri, 09 Jun 2023 07:00:00 GMT</pubDate>
      <author>Ravindrakumar Purohit</author>
    </item>
    <item>
      <title>A Dual-level Detection Method for Video Copy Detection</title>
      <description>&lt;a href=&quot;https://papers.cool/arxiv/2305.12361&quot;&gt;[Site]&lt;/a&gt; &lt;a href=&quot;https://papers.cool/query/2305.12361&quot;&gt;[Kimi]&lt;/a&gt; &lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Tianyi Wang&lt;/p&gt; &lt;p&gt;With the development of multimedia technology, Video Copy Detection has been a crucial problem for social media platforms. Meta AI hold Video Similarity Challenge on CVPR 2023 to push the technology forward. In this paper, we share our winner solutions on both tracks to help progress in this area. For Descriptor Track, we propose a dual-level detection method with Video Editing Detection (VED) and Frame Scenes Detection (FSD) to tackle the core challenges on Video Copy Detection. Experimental results demonstrate the effectiveness and efficiency of our proposed method. Code is available at https://github.com/FeipengMa6/VSC22-Submission.&lt;/p&gt; </description>
      <link>https://papers.cool/arxiv/2305.12361</link>
      <guid isPermaLink="false">https://papers.cool/arxiv/2305.12361</guid>
      <pubDate>Sun, 21 May 2023 06:19:08 GMT</pubDate>
      <author>Tianyi Wang</author>
    </item>
    <item>
      <title>A Comparative Study of Face Detection Algorithms for Masked Face Detection</title>
      <description>&lt;a href=&quot;https://papers.cool/arxiv/2305.11077&quot;&gt;[Site]&lt;/a&gt; &lt;a href=&quot;https://papers.cool/query/2305.11077&quot;&gt;[Kimi]&lt;/a&gt; &lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Sahel Mohammad Iqbal&lt;/p&gt; &lt;p&gt;Contemporary face detection algorithms have to deal with many challenges such as variations in pose, illumination, and scale. A subclass of the face detection problem that has recently gained increasing attention is occluded face detection, or more specifically, the detection of masked faces. Three years on since the advent of the COVID-19 pandemic, there is still a complete lack of evidence regarding how well existing face detection algorithms perform on masked faces. This article first offers a brief review of state-of-the-art face detectors and detectors made for the masked face problem, along with a review of the existing masked face datasets. We evaluate and compare the performances of a well-representative set of face detectors at masked face detection and conclude with a discussion on the possible contributing factors to their performance.&lt;/p&gt; </description>
      <link>https://papers.cool/arxiv/2305.11077</link>
      <guid isPermaLink="false">https://papers.cool/arxiv/2305.11077</guid>
      <pubDate>Thu, 18 May 2023 16:03:37 GMT</pubDate>
      <author>Sahel Mohammad Iqbal</author>
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    <item>
      <title>Context-Aware Chart Element Detection</title>
      <description>&lt;a href=&quot;https://papers.cool/arxiv/2305.04151&quot;&gt;[Site]&lt;/a&gt; &lt;a href=&quot;https://papers.cool/query/2305.04151&quot;&gt;[Kimi]&lt;/a&gt; &lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Pengyu Yan&lt;/p&gt; &lt;p&gt;As a prerequisite of chart data extraction, the accurate detection of chart basic elements is essential and mandatory. In contrast to object detection in the general image domain, chart element detection relies heavily on context information as charts are highly structured data visualization formats. To address this, we propose a novel method CACHED, which stands for Context-Aware Chart Element Detection, by integrating a local-global context fusion module consisting of visual context enhancement and positional context encoding with the Cascade R-CNN framework. To improve the generalization of our method for broader applicability, we refine the existing chart element categorization and standardized 18 classes for chart basic elements, excluding plot elements. Our CACHED method, with the updated category of chart elements, achieves state-of-the-art performance in our experiments, underscoring the importance of context in chart element detection. Extending our method to the bar plot detection task, we obtain the best result on the PMC test dataset.&lt;/p&gt; </description>
      <link>https://papers.cool/arxiv/2305.04151</link>
      <guid isPermaLink="false">https://papers.cool/arxiv/2305.04151</guid>
      <pubDate>Sun, 07 May 2023 00:08:39 GMT</pubDate>
      <author>Pengyu Yan</author>
    </item>
    <item>
      <title>AGAD: Adversarial Generative Anomaly Detection</title>
      <description>&lt;a href=&quot;https://papers.cool/arxiv/2304.04211&quot;&gt;[Site]&lt;/a&gt; &lt;a href=&quot;https://papers.cool/query/2304.04211&quot;&gt;[Kimi]&lt;/a&gt; &lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Jian Shi&lt;/p&gt; &lt;p&gt;Anomaly detection suffered from the lack of anomalies due to the diversity of abnormalities and the difficulties of obtaining large-scale anomaly data. Semi-supervised anomaly detection methods are often used to solely leverage normal data to detect abnormalities that deviated from the learnt normality distributions. Meanwhile, given the fact that limited anomaly data can be obtained with a minor cost in practice, some researches also investigated anomaly detection methods under supervised scenarios with limited anomaly data. In order to address the lack of abnormal data for robust anomaly detection, we propose Adversarial Generative Anomaly Detection (AGAD), a self-contrast-based anomaly detection paradigm that learns to detect anomalies by generating \textit{contextual adversarial information} from the massive normal examples. Essentially, our method generates pseudo-anomaly data for both supervised and semi-supervised anomaly detection scenarios. Extensive experiments are carried out on multiple benchmark datasets and real-world datasets, the results show significant improvement in both supervised and semi-supervised scenarios. Importantly, our approach is data-efficient that can boost up the detection accuracy with no more than 5% anomalous training data.&lt;/p&gt; </description>
      <link>https://papers.cool/arxiv/2304.04211</link>
      <guid isPermaLink="false">https://papers.cool/arxiv/2304.04211</guid>
      <pubDate>Sun, 09 Apr 2023 10:40:02 GMT</pubDate>
      <author>Jian Shi</author>
    </item>
    <item>
      <title>Entanglement detection with trace polynomials</title>
      <description>&lt;a href=&quot;https://papers.cool/arxiv/2303.07761&quot;&gt;[Site]&lt;/a&gt; &lt;a href=&quot;https://papers.cool/query/2303.07761&quot;&gt;[Kimi]&lt;/a&gt; &lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Albert Rico&lt;/p&gt; &lt;p&gt;We provide a systematic method for nonlinear entanglement detection based on trace polynomial inequalities. In particular, this allows to employ multi-partite witnesses for the detection of bipartite states, and vice versa. We identify witnesses for which linear detection of an entangled state fails, but for which nonlinear detection succeeds. With the trace polynomial formulation a great variety of witnesses arise from immamant inequalities, which can be implemented in the laboratory through randomized measurements.&lt;/p&gt; </description>
      <link>https://papers.cool/arxiv/2303.07761</link>
      <guid isPermaLink="false">https://papers.cool/arxiv/2303.07761</guid>
      <pubDate>Tue, 14 Mar 2023 10:06:34 GMT</pubDate>
      <author>Albert Rico</author>
    </item>
    <item>
      <title>Achieving Counterfactual Fairness for Anomaly Detection</title>
      <description>&lt;a href=&quot;https://papers.cool/arxiv/2303.02318&quot;&gt;[Site]&lt;/a&gt; &lt;a href=&quot;https://papers.cool/query/2303.02318&quot;&gt;[Kimi]&lt;/a&gt; &lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Xiao Han&lt;/p&gt; &lt;p&gt;Ensuring fairness in anomaly detection models has received much attention recently as many anomaly detection applications involve human beings. However, existing fair anomaly detection approaches mainly focus on association-based fairness notions. In this work, we target counterfactual fairness, which is a prevalent causation-based fairness notion. The goal of counterfactually fair anomaly detection is to ensure that the detection outcome of an individual in the factual world is the same as that in the counterfactual world where the individual had belonged to a different group. To this end, we propose a counterfactually fair anomaly detection (CFAD) framework which consists of two phases, counterfactual data generation and fair anomaly detection. Experimental results on a synthetic dataset and two real datasets show that CFAD can effectively detect anomalies as well as ensure counterfactual fairness.&lt;/p&gt; </description>
      <link>https://papers.cool/arxiv/2303.02318</link>
      <guid isPermaLink="false">https://papers.cool/arxiv/2303.02318</guid>
      <pubDate>Sat, 04 Mar 2023 04:45:12 GMT</pubDate>
      <author>Xiao Han</author>
    </item>
    <item>
      <title>Robust Detection Outcome: A Metric for Pathology Detection in Medical Images</title>
      <description>&lt;a href=&quot;https://papers.cool/arxiv/2303.01920&quot;&gt;[Site]&lt;/a&gt; &lt;a href=&quot;https://papers.cool/query/2303.01920&quot;&gt;[Kimi]&lt;/a&gt; &lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Felix Meissen&lt;/p&gt; &lt;p&gt;Detection of pathologies is a fundamental task in medical imaging and the evaluation of algorithms that can perform this task automatically is crucial. However, current object detection metrics for natural images do not reflect the specific clinical requirements in pathology detection sufficiently. To tackle this problem, we propose Robust Detection Outcome (RoDeO); a novel metric for evaluating algorithms for pathology detection in medical images, especially in chest X-rays. RoDeO evaluates different errors directly and individually, and reflects clinical needs better than current metrics. Extensive evaluation on the ChestX-ray8 dataset shows the superiority of our metrics compared to existing ones. We released the code at https://github.com/FeliMe/RoDeO and published RoDeO as pip package (rodeometric).&lt;/p&gt; </description>
      <link>https://papers.cool/arxiv/2303.01920</link>
      <guid isPermaLink="false">https://papers.cool/arxiv/2303.01920</guid>
      <pubDate>Fri, 03 Mar 2023 13:45:13 GMT</pubDate>
      <author>Felix Meissen</author>
    </item>
    <item>
      <title>Benchmarking Deepart Detection</title>
      <description>&lt;a href=&quot;https://papers.cool/arxiv/2302.14475&quot;&gt;[Site]&lt;/a&gt; &lt;a href=&quot;https://papers.cool/query/2302.14475&quot;&gt;[Kimi]&lt;/a&gt; &lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Yabin Wang&lt;/p&gt; &lt;p&gt;Deepfake technologies have been blurring the boundaries between the real and unreal, likely resulting in malicious events. By leveraging newly emerged deepfake technologies, deepfake researchers have been making a great upending to create deepfake artworks (deeparts), which are further closing the gap between reality and fantasy. To address potentially appeared ethics questions, this paper establishes a deepart detection database (DDDB) that consists of a set of high-quality conventional art images (conarts) and five sets of deepart images generated by five state-of-the-art deepfake models. This database enables us to explore once-for-all deepart detection and continual deepart detection. For the two new problems, we suggest four benchmark evaluations and four families of solutions on the constructed DDDB. The comprehensive study demonstrates the effectiveness of the proposed solutions on the established benchmark dataset, which is capable of paving a way to more interesting directions of deepart detection. The constructed benchmark dataset and the source code will be made publicly available.&lt;/p&gt; </description>
      <link>https://papers.cool/arxiv/2302.14475</link>
      <guid isPermaLink="false">https://papers.cool/arxiv/2302.14475</guid>
      <pubDate>Tue, 28 Feb 2023 10:34:44 GMT</pubDate>
      <author>Yabin Wang</author>
    </item>
    <item>
      <title>Towards Accurate Acne Detection via Decoupled Sequential Detection Head</title>
      <description>&lt;a href=&quot;https://papers.cool/arxiv/2301.12219&quot;&gt;[Site]&lt;/a&gt; &lt;a href=&quot;https://papers.cool/query/2301.12219&quot;&gt;[Kimi]&lt;/a&gt; &lt;p&gt;&lt;b&gt;Authors:&lt;/b&gt; Xin Wei&lt;/p&gt; &lt;p&gt;Accurate acne detection plays a crucial role in acquiring precise diagnosis and conducting proper therapy. However, the ambiguous boundaries and arbitrary dimensions of acne lesions severely limit the performance of existing methods. In this paper, we address these challenges via a novel Decoupled Sequential Detection Head (DSDH), which can be easily adopted by mainstream two-stage detectors. DSDH brings two simple but effective improvements to acne detection. Firstly, the offset and scaling tasks are explicitly introduced, and their incompatibility is settled by our task-decouple mecha