GEB+: A Benchmark for Generic Event Boundary Captioning, Grounding and Retrieval

GEB+: A Benchmark for Generic Event Boundary Captioning, Grounding and Retrieval [PDF], ECCV 2022

Yuxuan Wang, Difei Gao, Licheng Yu, Stan Weixian Lei, Matt Feiszli, and Mike Zheng Shou

We introduce a new dataset called Kinetics-GEB+. The dataset consists of over 170k boundaries associated with captions describing status changes in the generic events in 12K videos. Upon this new dataset, we propose three tasks (Boundary Captioning, Boundary Grounding and Boundary Caption-Video Retrieval) supporting the development of a more fine-grained, robust, and human-like understanding of videos through status changes.

We evaluate many representative baselines in our dataset, where we also design a new TPD (Temporal-based Pairwise Difference) Modeling method for visual difference and achieve significant performance improvements. Besides, the results show there are still formidable challenges for current methods in the utilization of different granularities, representation of visual difference, and the accurate localization of status changes. Further analysis shows that our dataset can drive developing more powerful methods to understand status changes and thus improve video level comprehension.

Using Kinetics-GEB+ Dataset

In our Kinetics-GEB+ dataset, each video contains 1 to 8 annotations from different annotators and each annotation consists of several boundaries inside a video, where the boundaries' location are not the same. In the evaluation of downstream tasks, we select one annotator whose labeled boundaries are most consistent with others to reduce noise and duplication. Then, we use these boundaries’ timestamps as the anchors to merge other annotators’ captions, preserving the diversity of different opinions. Thus, one video corresponds to multiple boundaries, and each boundary could be with multiple captions. Finally, this selection includes 40k anchors from all videos.

Here we release 2 versions of dataset:

a) Filtered datasets (Recommended) [Download] used in our paper, which has been adjusted for downstream task, including 40K filtered boundaries.

b) Raw annotation [Download] that could be used as supplement in training your own model, including 170K boundaries.

Note that our paper uses version a) in the evaluation of our model, please also evaluated your own model with version a) in future comparisons.

Prepare to Use Our Baseline Models

Clone the project to run our baseline models:

git clone https://github.com/Yuxuan-W/GEB-Plus.git

Clone our conda environment using:

conda env create -n ENVNAME --file environment.yml

Note that the version of pytorch-transformer we use is 1.0.0.

Task1: Boundary Captioning

Preparing evaluation package

To run Boundary Captioning task, you need to download the evaluation package [Download] and put it under utils folder as:

GEBC/utils/pycocoevalcap

Note that the evaluation package also requires Java, one simple way is to install a light-weight open-jdk on your server if you haven't installed.

Preparing features

To run Boundary Captioning task, you need to download and unzip the features [Download], make sure you have the following path:

GEBC/datasets/features/region_feature

GEBC/datasets/features/tsn_captioning_feature

Training from scratch

To train on the captioning baseline, execute the following command:

python run_captioning.py --do_train --do_test --do_eval --ablation obj --evaluate_during_training

Testing our trained model

We only provide the checkpoint that generating our highest score in the paper [Download]. Unzip the folder to your project, execute the following command:

python run_captioning.py --do_test --do_eval --ablation obj --eval_model_dir $YOUR_UNZIPPED_DIR$

Performance of our baseline

The best performance of our baseline are achieved by ActBERT-revised with ResNet-roi+TSN feature:

	Subject	Status Before	Status After	Average
CIDEr	85.33	75.98	62.82	74.71
SPICE	20.10	20.66	17.81	19.52
ROUGE_L	39.16	23.70	21.60	28.15

Task2: Boundary Grounding

Like we mentioned in the paper, we use two schemes of frame sampling when proposing the timestamp candidates who might be the answer. By default, we sampled one candidates every 3 frames (0.1s), or we used the baseline of GEBD to generate proposals. Here we provide implementations for both of them.

Preparing features

To run Boundary Grounding task, you need to download and unzip the features [Download], make sure you have the following path:

GEBC/datasets/features/region_feature

If not using GEBD proposals (By Default), you will need: GEBC/datasets/features/tsn_all1s_feature

If using GEBD proposals, you will need: GEBC/datasets/features/tsn_gebd_feature

Training from scratch

To train on the grounding baseline, execute the following command:

python run_grounding.py --do_train --do_test --do_eval --ablation obj --evaluate_during_training (By Default)

Or if you want to use GEBD proposals in followed validation and testing after training is finished:

python run_grounding.py --do_train --do_test --do_eval --use_gebd --ablation obj --evaluate_during_training (Use GEBD)

Testing our trained model

We only provide the checkpoint that generating our highest score in the paper [Download]. Unzip the folder to your project, execute the following command:

python run_grounding.py --do_test --do_eval --ablation obj --eval_model_dir $YOUR_UNZIPPED_DIR$ (By Default)

Or if you want to use GEBD proposals in testing:

python run_grounding.py --do_test --do_eval --use_gebd --ablation obj --eval_model_dir $YOUR_UNZIPPED_DIR$ (Use GEBD)

Performance of our baseline

The best performance of our baseline are achieved by FROZEN-revised with ResNet-roi+TSN feature:

Threshold(s)	0.1	0.2	0.3	1	1.5	2	2.5	3	Average
Default	4.28	8.54	18.33	31.04	40.48	47.86	54.81	61.45	33.35
Use GEBD	4.20	8.48	18.49	29.91	39.54	48.37	55.29	61.55	33.32

Task3: Boundary Caption-Text Retrieval

Preparing features

To run Boundary Grounding task, you need to download and unzip the features [Download], make sure you have the following path:

GEBC/datasets/features/region_feature

GEBC/datasets/features/tsn_gebd_feature

Training from scratch

To train on the grounding baseline, execute the following command:

python run_retrieval.py --do_train --do_test --do_eval --ablation obj --evaluate_during_training

Testing our trained model

We only provide the checkpoint that generating our highest score in the paper [Download]. Unzip the folder to your project, execute the following command:

python run_retrieval.py --do_test --do_eval --ablation obj --eval_model_dir $YOUR_UNZIPPED_DIR$

Performance of our baseline

The best performance of our baseline are achieved by FROZEN-revised with ResNet-roi+TSN feature extracted following the timestamps proposals generated by GEBD baseline:

Metric	mAP	R@1	R@5	R@10	R@50
Use GEBD	23.39	12.80	34.81	45.66	68.1

Citation

If you find our work helps, please cite our paper:

@article{wang2022generic,
  title={Generic Event Boundary Captioning: A Benchmark for Status Changes Understanding},
  author={Wang, Yuxuan and Gao, Difei and Yu, Licheng and Lei, Stan Weixian and Feiszli, Matt and Shou, Mike Zheng},
  journal={arXiv preprint arXiv:2204.00486},
  year={2022}
}

Contact

This repo is maintained by Yuxuan Wang. Questions and discussions are welcome via [email protected].

Acknowledgement

This project is supported by the National Research Foundation, Singapore under its NRFF Award NRF-NRFF13-2021-0008, and Mike Zheng Shou's Start-Up Grant from NUS. The computational work for this article was partially performed on resources of the National Supercomputing Centre, Singapore.

Thanks to Difei Gao, Licheng Yu, and the great efforts contributed by other excellent staffs from Meta AI.