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T2VLAD

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Contents

• Introduction
• Data
• Train
• Test
• Reference

Before getting started, you need to install additional dependencies as follows:

python -m pip install paddlenlp

Introduction

T2VLAD is proposed by Baidu in CVPR2021 for text-video retrieval. Text-video retrieval is a challenging task that aims to search relevant video contents based on natural language descriptions. The key to this problem is to measure text- video similarities in a joint embedding space. T2VLAD designs an efficient global-local alignment method. This model achieves consistent improvements on three standard text-video retrieval benchmarks and outperform the state- of-the-art by a clear margin.

Data

Please refer to MSR-VTT data download and preparation doc MSR-VTT data

Train

Train on MSR-VTT

Download data then move to data/MSRVTT folder.

Start training

• Train T2VLAD on MSRVTT scripts:

export CUDA_VISIBLE_DEVICES=0
python3.7 train.py --config ./configs/msrvtt_transformers.json

T2VLAD uses the Ranger optimizer during training. We haven't supported the implementation of Ranger optimizer, for now, the AdamW optimizer can be used to complete the training.

Test

• Evaluation performs on downstream task, i.e. text-video clip retrieval on MSR-VTT dataset, test accuracy can be obtained using scripts:

export CUDA_VISIBLE_DEVICES=0
python3.7 test.py --config ./configs/msrvtt_transformers.json --resume ./T2VLAD_msrvtt.pdparams

Accuracy on MSR-VTT: Text $\rightarrow$ Video

R@1	R@5	R@10	Median R	checkpoints
29.5	59.0	70.1	4	T2VLAD.pdparams

Video $\rightarrow$ Text

R@1	R@5	R@10	Median R
26.1	54.7	68.1	4

Reference

• T2VLAD: Global-Local Sequence Alignment for Text-Video Retrieval , Xiaohan Wang, Linchao Zhu, Yi Yang