ECCV2022: LAFF for Text-to-Video Retrieval

This is the official source code of our LAFF paper: Lightweight Attentional Feature Fusion: A New Baseline for Text-to-Video Retrieval.

Environment

We used Anaconda to setup a deep learning workspace that supports PyTorch. Run the following script to install all the required packages.

conda create -n laff python==3.8 -y
conda activate laff
git clone https://github.com/ruc-aimc-lab/laff.git
cd laff
pip install -r requirements.txt

Downloads

Data

See the data page.

Trained Models

Provide model links here.

Code

The shell folder provides scripts that perform training from scratch.

Performance

• Video features: clip-ft, x3d, ircsn and tf.
• Text features: clip-ft, bow, w2v and gru.

MV-test3k

Model	R1	R5	R10	Medr
W2VV++	23.0	49.0	60.7	6
SEA	19.9	44.3	56.5	7
CLIP-finetuned	27.7	53.0	64.2	5
LAFF	28.0	53.8	64.9	4
LAFF-ml	29.1	54.9	65.8	4

MV-test1k

Model	R1	R5	R10	Medr
W2VV++	39.4	68.1	78.1	2
SEA	37.2	67.1	78.3	2
CLIP-finetuned	39.7	67.8	78.4	2
LAFF	42.2	70.7	81.2	2
LAFF-ml	42.6	71.8	81	2

MSVD

Model	R1	R5	R10	Medr
W2VV++	37.8	71.0	81.6	2
SEA	34.5	68.8	80.5	3
CLIP-finetuned	44.6	74.7	84.1	2
LAFF	45.2	75.8	84.3	2
LAFF-ml	45.4	76.0	84.6	2

TGIF

Model	R1	R5	R10	Medr
W2VV++	22	42.8	52.7	9
SEA	16.4	33.6	42.5	17
CLIP-finetuned	21.5	40.6	49.9	11
LAFF	24.1	44.7	54.3	8
LAFF-ml	24.5	45.0	54.5	8

VATEX

Model	R1	R5	R10	Medr
W2VV++	55.8	91.2	96	1
SEA	52.4	90.2	95.9	1
CLIP-finetuned	53.3	87.5	94.0	1
LAFF	57.7	91.3	95.9	1
LAFF-ml	59.1	91.7	96.3	1

Citation

@inproceedings{eccv2022-laff,
title = {Lightweight Attentional Feature Fusion: A New Baseline for Text-to-Video Retrieval},
author = {Fan Hu and Aozhu Chen and Ziyue Wang and Fangming Zhou and Jianfeng Dong and Xirong Li},
year = {2022},
booktitle = {ECCV},
}

Contact

If you enounter any issue when running the code, please feel free to reach us either by creating a new issue in the github or by emailing

• Fan Hu (hufan_hf@ruc.edu.cn)
• Ziyue Wang (wangziyueamy@sina.com)
• Aozhu Chen (caz@ruc.edu.cn)