[ECCV 2024 Accepted]Goldfish: Vision-Language Understanding of Arbitrarily Long Videos

[CVPR2024 workshop]MiniGPT4-Video: Advancing Multimodal LLMs for Video Understanding with Interleaved Visual-Textual Tokens

This repo contains the codes for MiniGPT4-video for short video understanding and Goldfish for long video understanding.

Online Demos

Project Page 📝 arXiv Paper 🤗 TVQA-Long Dataset

🎞️ Project Page 📝 arXiv Paper

Overview

Most current LLM-based models for video understanding can process videos within minutes but struggle with processing lengthy videos due to the “noise and redundancy challenge” and “memory and compu- tation” challenges. In this paper, we present Goldfish, a methodology tailored for comprehending videos of arbitrary lengths. We also introduce the TVQA-long benchmark, specifically designed to evaluate models’ capabilities in understanding long videos with questions in both vision and text content. Goldfish approaches these challenges with an efficient retrieval mechanism that initially gathers the top-k video clips relevant to the instruction before proceeding to provide the desired response. This de- sign of the retrieval mechanism enables the Goldfish to efficiently process arbitrarily long video sequences, facilitating its application in contexts such as movies or television series. To facilitate the retrieval process, we developed MiniGPT4-Video that generates detailed descriptions for the video clips. In addressing the scarcity of benchmarks for long video evalu- ation, we adapted the TVQA short video benchmark for extended content analysis by aggregating questions from entire episodes, thereby shifting the evaluation from partial to full episode comprehension. We attained a 41.78% accuracy rate on the TVQA-long benchmark, surpassing previous methods by 14.94%. Our MiniGPT4-Video also shows exceptional perfor- mance in short video comprehension, exceeding existing state-of-the-art methods by 3.23%, 2.03%, 16.5% and 23.59% on the MSVD, MSRVTT, TGIF,and TVQA short video benchmarks, respectively. These results indicate that our models have significant improvements in both long and short-video understanding.

Goldfish framework (Long videos)

MiniGPT4-Video (Short videos)

🚀 Demo

1. Clone the repository

git clone https://github.com/Vision-CAIR/MiniGPT4-video.git
cd MiniGPT4-video

2. Set up the environment

conda env create -f environment.yml

3. Download the checkpoints

MiniGPT4-Video (Llama2 Chat 7B)	MiniGPT4-Video (Mistral 7B)
Download	Download

4. Run the demo
Goldfish demo

# For recommended performance, add the parameter --use_openai_embedding True to the command below and set the API key in the environment variable OPENAI_API_KEY otherwise the model will use the default embeddings.
export OPENAI_API_KEY="your_openai_key" 
# Llama2
python goldfish_demo.py --ckpt path_to_video_checkpoint --cfg-path test_configs/llama2_test_config.yaml 
# Mistral
python goldfish_demo.py --ckpt path_to_video_checkpoint --cfg-path test_configs/mistral_test_config.yaml

MiniGPT4-Video demo

# Llama2
python minigpt4_video_demo.py --ckpt path_to_video_checkpoint --cfg-path test_configs/llama2_test_config.yaml
# Mistral
python minigpt4_video_demo.py --ckpt path_to_video_checkpoint --cfg-path test_configs/mistral_test_config.yaml

Inference

Do the previous steps and replace step 4 with this step
Goldfish inference

# For recommended performance, add the parameter --use_openai_embedding True to the command below and set the API key in the environment variable OPENAI_API_KEY otherwise the model will use the default embeddings.
export OPENAI_API_KEY="your_openai_key" 
# Llama2
python goldfish_inference.py --ckpt path_to_llama2_checkpoint --cfg-path test_configs/llama2_test_config.yaml --video_path path_to_video --question "Your question here" 
# Mistral
python goldfish_inference.py --ckpt path_to_mistral_checkpoint --cfg-path test_configs/mistral_test_config.yaml --video_path path_to_video --question "Your question here" 

MiniGPT4-Video inference

# Llama2
python minigpt4_video_inference.py --ckpt path_to_llama2_checkpoint --cfg-path test_configs/llama2_test_config.yaml --video_path path_to_video --question "Your question here" 
# Mistral
python minigpt4_video_inference.py --ckpt path_to_mistral_checkpoint --cfg-path test_configs/mistral_test_config.yaml --video_path path_to_video --question "Your question here" 

🔥 Training

For both Goldfish and MiniGPT4-Video, the only training part is the MiniGPT4-Video model.

To customize MiniGPT4-Video for your own Video-text dataset

You can find the steps to customize MiniGPT4-Video for your own video-text dataset in Custom_training.md

Training datasets

After downloading the datasets below, you should go to the datasets configuration folder here minigpt4/configs/datasets set the paths for each dataset there.
Image text training
You can find the steps to download the datasets in MiniGPT4

• LAION
  
• Conceptual Captions
  
• SBU
  

Video text training:

• CMD
  
• Webvid
  
• Video Instructional Dataset 100K
  

You can find the datasets annotation files for video_text datasets here download

Model training:

You can edit the number of gpus in the each script.sh below

Stage 1 (image text pretraining)

You can directly download the pretrained MiniGPT4 checkpoint aligned with Llama2.

Or train by yourself:

# pretrain
# Llama2
torchrun --nproc-per-node NUM_GPU train.py --cfg-path train_configs/224_minigpt4_llama2_image.yaml
# Mistral
torchrun --nproc-per-node NUM_GPU train.py --cfg-path train_configs/224_minigpt4_mistral_image.yaml

# align
# To launch the second stage alignment, first specify the path to the checkpoint file trained in pretrain stage.
# Llama2
torchrun --nproc-per-node NUM_GPU train.py --cfg-path train_configs/224_minigpt4_llama2_image_align.yaml
# Mistral
torchrun --nproc-per-node NUM_GPU train.py --cfg-path train_configs/224_minigpt4_mistral_image_align.yaml

You can download our trained weights for this stage from here Llama2 Mistral

Stage 2 (video captioning pretraining)

For Llama2
set the cfg-path in the script to train_configs/224_v2_llama2_video_stage_2.yaml
set the model name here minigpt4/configs/datasets/cmd_video/default.yaml and minigpt4/configs/datasets/webvid/default.yaml to llama2
For Mistral
set the cfg-path in the script to train_configs/224_v2_mistral_video_stage_2.yaml
set the model name here minigpt4/configs/datasets/cmd_video/default.yaml and minigpt4/configs/datasets/webvid/default.yaml to mistral

bash jobs_video/train/stage_2.sh

You can download our trained weights for this stage from here Llama2 Mistral

Stage 3 (video Instruction finetuning)

For Llama2
set the cfg-path in the script to train_configs/224_v2_llama2_video_stage_3.yaml
set the model name here minigpt4/configs/datasets/video_chatgpt/default.yaml to llama2

For Mistral
set the cfg-path in the script to train_configs/224_v2_mistral_video_stage_3.yaml
set the model name here minigpt4/configs/datasets/video_chatgpt/default.yaml to mistral

bash jobs_video/train/stage_3.sh

You can download our trained weights for this stage from here Llama2 Mistral

⚡ MiniGPT4-Video Evaluation

To reproduce the results use the best checkpoints for each model
Llama2 Mistral
We used the same evaluation as Video-ChatGPT

Method	Using Subtitles	Information Correctness	Detailed Orientation	Contextual Understanding	Temporal Understanding	Consistency
LLaMA Adapter	❌	2.03	2.32	2.30	1.98	2.15
Video LLaMA	❌	1.96	2.18	2.16	1.82	1.79
Video Chat	❌	2.23	2.50	2.53	1.94	2.24
Video-ChatGPT	❌	2.40	2.52	2.62	1.98	2.37
BT-Adapter-7B	❌	2.68	2.69	3.27	2.34	2.46
LLaMA-VID-7B	❌	2.96	3.00	3.53	2.46	2.51
Ours-7B Llama2	❌	2.93	2.97	3.45	2.47	2.60
Ours-7B Llama2	✅	3.08	3.02	3.57	2.65	2.67
Ours-7B Mistral	❌	2.83	2.52	3.01	2.32	2.40
Ours-7B Mistral	✅	2.91	2.57	3.11	2.33	2.39

Method	Using Subtitles	MSVD Acc.↑	MSVD Score↑	MSRVTT Acc.↑	MSRVTT Score↑	TGIF Acc.↑	TGIF Score↑	ActivityNet Acc.↑	ActivityNet Score↑	TVQA Acc.↑
FrozenBiLM	❌	32.2	--	16.8	--	41	--	24.7	--	29.7
LLaMA Adapter	❌	54.9	3.1	43.8	2.7	--	--	34.2	2.7	--
Video LLaMA	❌	51.6	2.5	29	1.8	--	--	12.4	1.1	--
Video Chat	❌	56.3	2.8	45	2.5	34.4	2.3	26.5	2.2	--
Video-ChatGPT	❌	64.9	3.3	49.3	2.8	51.4	3.0	35.2	2.7	23.35
BT-Adapter-7B	❌	67.7	3.7	57	3.2	--	--	45.7	3.2	--
LLaMA-VID-7B	❌	69.7	3.7	57.7	3.2	--	--	47.4	3.3	--
Ours-7B LLama2	❌	72.93	3.84	58.83	3.29	67.9	3.71	45.85	3.23	36.45
Ours-7B Llama2	✅	72.93	3.84	59.73	3.3	67.9	3.71	46.3	3.4	46.94
Ours-7B Mistral	❌	73.92	4.06	58.26	3.52	72.22	4.08	44.25	3.35	33.90
Ours-7B Mistral	✅	73.92	4.06	58.68	3.53	72.22	4.08	44.38	3.36	54.21

Download datasets for evaluation

• MSVD
  
• MSRVTT
  
• TGIF
  
• ActivityNet
  
• TVQA
  
• Video-ChatGPT benchmark
  

You can find the evaluation datasets annotation files download

Run evaluation script

Set the each evaluation script parameters to include the path to the checkpoints, the dataset name and whether to use subtitles or not

# Llama2
bash jobs_video/eval/llama2_evaluation.sh
# Mistral
bash jobs_video/eval/mistral_evalualtion.sh

Then Use GPT3.5 turbo to compare the predictions with the ground truth and generate the accuracy and scores
Set these variables in both evaluate_benchmark.sh and evaluate_zeroshot.sh

PRED="path_to_predictions"
OUTPUT_DIR="path_to_output_dir"
API_KEY="openAI_key"
NUM_TASKS=128

Then to evaluate [Video-ChatGPT benchmark] run the following script

bash test_benchmark/quantitative_evaluation/evaluate_benchmark.sh

To evaluate open ended questions run the following script

bash test_benchmark/quantitative_evaluation/evaluate_zeroshot.sh

⚡ Goldfish Evaluation

Long video benchmarking results on four benchmarks: LLama-Vid, MovieChat, Movie QA, and our proposed TVQA-Long. The "V" modality indicates the use of video frames only, while "V+T" indicates the use of both video frames and subtitles

Method	Modalities	LLama-Vid Acc.↑	LLama-Vid Score↑	MovieChat Acc.↑	MovieChat Score↑	Movie QA Acc.↑	Movie QA Score↑	TVQA-Long Acc.↑	TVQA-Long Score↑
LLAMA-VID	V	20.68	2.41	53.2	3.81	24.42	2.19	24.63	2.16
MovieChat	V	11.71	1.45	NA	NA	16.18	1.68	5.0	0.86
Ours	V	23.09	2.19	67.6	4.23	28.49	2.8	28.61	2.78
LLAMA-VID	V+T	41.4†	3.07†	NA	NA	37.65†	3.03†	26.86	2.21
Ours	V+T	31.49	2.48	NA	NA	35.24	3.1	41.78	3.21

Note: The dagger † symbol indicates the method used the benchmark during training, which implies unfair comparison.

To reproduce the results use the checkpoints/video_llama_checkpoint_last.pth and use openAI embedding --use_openai_embedding=True

Download datasets for evaluation

For Llama-vid and MovieQA
Dowlnoad the original MovieNet data with movies and annotations from here
This will be the souce videos for LLama-vid and MovieQA

Filtered Annotations same as illestrated in the paper and used for evaluation

Llama-vid
MovieQA
For Moviechat the only available videos while implementing this work is 10 % of the training data and this what we used for evalaution and can be found here
Full dataset can be found here
For TVQA-Long
Both videos and annotations can be found here TVQA-Long

Run the evaluation scripts

# Llama-vid evalauation 
# set these parameters in the script 
videos_path="path to the videos"
subtitle_path="path to the subtitles"
video_clips_saving_path="path to save the video clips"
annotation_file="path to the annotation file"
movienet_annotations_dir="path to the movienet annotations directory" 
NEIGHBOURS=3
use_openai_embedding="whether to use openai embeddings or not"
# then run the script
bash jobs_video/long_video_eval/movies/eval_model_summary_llama_vid.sh

# MovieQA evaluation
# same as above but set the parameters in the script to the MovieQA paths 
bash jobs_video/long_video_eval/movies/eval_model_summary_movie_qa.sh

# MovieChat evaluation 
# set these parameters in the script 
dataset_path="path to the movies folder"
annotation_json_folder="path to the jsons folder"
# then run the script
bash jobs_video/long_video_eval/movies/eval_model_summary_movie_chat.sh

TVQA-Long

For Goldfish evaluation we can use the original separated clips from the original TVQA dataset
Download the original TVQA videos for short videos from here

# set these parameters in the script
tvqa_json_subtitles="path to the tvqa json subtitles file"
tvqa_clips_subtitles="path to the tvqa clips subtitles"
videos_frames="path to the video frames"
annotation_path="path to the TVQA-Long annotation file"
NEIGHBOURS= 3
use_openai_embedding="whether to use openai embeddings or not"
# then run the script
bash jobs_video/long_video_eval/tvqa_eval/eval_model_summary.sh

Then Use GPT3.5 turbo to compare the predictions with the ground truth and generate the accuracy and scores
Set these variables in evaluate_zeroshot.sh

PRED="path_to_predictions"
OUTPUT_DIR="path_to_output_dir"
API_KEY="openAI_key"
NUM_TASKS=128

To evaluate open ended questions run the following script

bash test_benchmark/quantitative_evaluation/evaluate_zeroshot.sh

Citation

If you're using MiniGPT4-Video or Goldfish in your research or applications, please cite using this BibTeX:

@misc{ataallah2024goldfishvisionlanguageunderstandingarbitrarily,
      title={Goldfish: Vision-Language Understanding of Arbitrarily Long Videos}, 
      author={Kirolos Ataallah and Xiaoqian Shen and Eslam Abdelrahman and Essam Sleiman and Mingchen Zhuge and Jian Ding and Deyao Zhu and Jürgen Schmidhuber and Mohamed Elhoseiny},
      year={2024},
      eprint={2407.12679},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2407.12679}, 
}
@article{ataallah2024minigpt4,
  title={MiniGPT4-Video: Advancing Multimodal LLMs for Video Understanding with Interleaved Visual-Textual Tokens},
  author={Ataallah, Kirolos and Shen, Xiaoqian and Abdelrahman, Eslam and Sleiman, Essam and Zhu, Deyao and Ding, Jian and Elhoseiny, Mohamed},
  journal={arXiv preprint arXiv:2404.03413},
  year={2024}
}

Acknowledgements

MiniGPT4
Video-ChatGPT

License

This repository is under BSD 3-Clause License. Many codes are based on MiniGPT4.