| 📃 Paper |

Welcome to the official repository of ViLCo-Bench!

This repository is the first dedicated benchmark designed to evaluate continual learning models across various video-text tasks. Unlike traditional continual learning tasks, ViLCo goes beyond classification and introduces challenges of cross-modal inference and temporal complexity in videos. We present three unique video-language continual learning tasks including:

• Moment Query (MQ),
• Natural Language Query (NLQ), and
• Vision Query (VQ).

Also, we provide a curated dataset with annotations for each episodic memory task. Additionally, we propose novel memory-efficient models tailored for ViLCo-Bench.

In this repository, we provide the source code, access to the dataset, prepare the data, and the source code for training and evaluating ViLCo and other baselines across the benchmark.

• 🌟 Abstract
• 📂 Repository Contents
• 🛠️ Benchmark and Framework
  • Dataset Summary
  • Leaderboard
• 🚀 Getting Started
• 📚 Citation

Video language continual learning involves continuously adapting to information from video and text inputs, enhancing a model’s ability to handle new tasks while retaining prior knowledge. This field is a relatively under-explored area, and establishing appropriate datasets is crucial for facilitating communication and research in this field. In this study, we present the first dedicated benchmark, ViLCo-Bench, designed to evaluate continual learning models across a range of video-text tasks. The dataset comprises ten-minute-long videos and corresponding language queries collected from publicly available datasets. Additionally, we introduce a novel memory-efficient framework that incorporates self-supervised learning and mimics long-term and short-term memory effects. This framework addresses challenges including memory complexity from long video clips, natural language complexity from open queries, and text-video misalignment. We posit that ViLCo-Bench, with greater complexity compared to existing continual learning benchmarks, would serve as a critical tool for exploring the video-language domain, extending beyond conventional class-incremental tasks, and addressing complex and limited annotation issues.

• Dataset: Ten-minute-long videos and corresponding language queries collected from publicly available datasets.
• Benchmark Tasks: Scripts and configurations to evaluate continual learning models on various video-text tasks.
• Framework: Implementation of ViLCo, our novel memory-efficient framework incorporating self-supervised learning.
• Documentation: Detailed documentation to help you get started with using the ViLCo-Bench benchmark.

We used Ego4D dataset consisting of 3670 hours of egocentric videos. We selected videos and their corresponding queries and narrations to create three subsets for continual learning: ViLCo-Bench-MQ, ViLCo-Bench-NLQ, and ViLCo-Bench-VQ.

Before you begin, ensure you have met the following requirements:

• Python 3.8 or higher
• Required Python packages (see requirements.txt)

Clone this repository and install the necessary dependencies:

   conda create --name vilco python=3.8
   conda activate vilco

   # Install pytorch or use your own torch version
   conda install pytorch==1.12.0 torchvision==0.13.0 torchaudio==0.12.0 cudatoolkit=11.6 -c pytorch -c conda-forge

   git clone https://github.com/cruiseresearchgroup/ViLCo.git
   cd ViLCo
   pip install -r requirements.txt

• NMS compilation

cd ./MQ/libs/utils
python setup.py install --user
cd ../..

The repository is organized as follows:

ViLCo-Bench
├── MQ
│   └── Feature extraction, Fine-tuning on EgoMQ with query-incremental setting 
├── NLQ
│   └── Feature extraction and head-tuning on EgoNLQ with query-incremental setting 
└── VQ
    └── Head-tuning on EgoVQ with query-incremental setting 

Each directory contains data preparation, training/inference scripts, and we provided pre-extracted video and text features for new CL tasks.

We provide metadata for the proposed MQ/NLQ/MQ tasks, detailing data subsets aligned with the tasks in each CL setup. To facilitate a quick start, we include pre-extracted visual and textual features. Additionally, we offer instructions for preparing the full datasets, allowing for easy extension to custom multi-modal data sources.

Downdown pre-extracted features from zenodo Link. Note that you should download all files in your folder, then run unzip ViLCo_data.zip.

ViLCo_data
├── feature_extraction
│   └── Feature extraction script for custom dataset, download scripts for additional visual features
├── features
│   └── pre-extratced visual features.
├── MQ_meta_data
│   └── metadata for moments query.
├── NLQ_meta_data
│   └── metadata for natural language query.
├── VQ_meta_data
│   └── metadata for visual query.
└── narration_data
    └── pre-extracted narration features for self-supervised training.

cd feature_extraction

python download_features.py --feature_types egovlp

python convert_pt_to_lmdb.py

wget https://www.cis.jhu.edu/~shraman/EgoVLPv2/pre-extracted_features/EgoMQ/EgoVLPv2.tgz
tar -xvzf EgoVLPv2.tgz && rm EgoVLPv2.tgz

1. Download original video data from Ego4d. Pleas refer to Start Here to download the full videos and their corresponding annotations. After you download the CLI, you could run command ego4d --output_directory="~/ego4d_data" --datasets full_scale annotations.

2. Extract visual/textual features for data pre-processing. In our paper, we finally use EgoVLP-v2 features. Also, the combination of multiple visual features can achieve good results.

   Details:

   For offline data prepartion, please follow the same steps as below:

   In MQ, use python convert_annotation.py to convert the official annotation to the processed one. And put it into MQ/data/ego4d. create a config file corresponding to training. And put it into MQ/configs/.

   In NLQ, please refer to File_Precess_RM.

   In VQ, please refer to the five steps in Preparing Dataset.

3. Generate metadata for specific tasks. we provide corresponding scripts in scripts\. Put the python file into its corresponding task dictionary and change the detailed data path.

Run the following command to train and evaluate the model across MQ tasks:

    bash train_cl.sh mq_vilco DEVICE_NO PORT_NO

(taking MQ as an example), it will automatically validate performance on val-set (e.g., average mAP, Recall@1x).

For expanding ViLCo to custom data/tasks, please refer to Add New Task.

We welcome contributions to the ViLCo-Bench benchmark! Please feel free to join the leaderboard, open issues, submit pull requests, or provide suggestions.

ViLCo is licensed under a MIT License.

This code is inspired by vCLIMB. We develop the benchmark model, inspired by EgoVLP, EgoVLP-v2, Ego4D-ASL, Ground-NLQ, VQLoC. Thanks for their contributions.

This material is based upon work supported by the International Technology Center Indo-Pacific343 (ITC-IPAC) under Contract No. FA520923C0020.

If you use ViLCo-Bench in your research, please cite our paper:

@article{Tang2024vilco,
  title={ViLCo-Bench: VIdeo Language COntinual learning Benchmark},
  author={Tianqi Tang, Shohreh Deldari, Hao Xue, Celso De Melo, Flora Salim},
  journal={Under review},
  year={2024},
}

Thank you for using ViLCo-Bench! We hope this benchmark serves as a valuable resource for your research in video-language continual learning.