Video Retrieval Models

CLIP4Clip

CLIP4Clip: An Empirical Study of CLIP for End to End Video Clip Retrieval

Abstract

Video-text retrieval plays an essential role in multi-modal research and has been widely used in many real-world web applications. The CLIP (Contrastive Language-Image Pre-training), an image-language pre-training model, has demonstrated the power of visual concepts learning from web collected image-text datasets. In this paper, we propose a CLIP4Clip model to transfer the knowledge of the CLIP model to video-language retrieval in an end-to-end manner. Several questions are investigated via empirical studies: 1) Whether image feature is enough for video-text retrieval? 2) How a post-pretraining on a large-scale video-text dataset based on the CLIP affect the performance? 3) What is the practical mechanism to model temporal dependency between video frames? And 4) The Hyper-parameters sensitivity of the model on video-text retrieval task. Extensive experimental results present that the CLIP4Clip model transferred from the CLIP can achieve SOTA results on various video-text retrieval datasets, including MSR-VTT, MSVC, LSMDC, ActivityNet, and DiDeMo.

Results and Models

MSRVTT-9k

frame sampling strategy	resolution	gpus	backbone	adapter	pretrain	Recall@1	Recall@5	Recall@10	MdR	MnR	testing protocol	config	ckpt	log
uniform 12	224x224	8	ViT-B/32	Mean	clip	43.1	69.4	78.9	2.0	16.8	1 clips x 1 crop	config	ckpt	log

For more details on data preparation, you can refer to video_retrieval.

Train

You can use the following command to train a model.

python tools/train.py ${CONFIG_FILE} [optional arguments]

Example: train CLIP4Clip model on MSRVTT-9k dataset in a deterministic option with periodic validation.

python tools/train.py configs/retrieval/clip4clip/clip4clip_vit-base-p32-res224-clip-pre_8xb16-u12-5e_msrvtt-9k-rgb.py \
    --seed 0 --deterministic

For more details, you can refer to the Training part in the Training and Test Tutorial.

Test

You can use the following command to test a model.

python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [optional arguments]

Example: test CLIP4Clip model on MSRVTT-9k dataset and dump the result to a pkl file.

python tools/test.py configs/retrieval/clip4clip/clip4clip_vit-base-p32-res224-clip-pre_8xb16-u12-5e_msrvtt-9k-rgb.py \
    checkpoints/SOME_CHECKPOINT.pth --dump result.pkl

For more details, you can refer to the Test part in the Training and Test Tutorial.

Citation

@article{luo2022clip4clip,
  title={CLIP4Clip: An empirical study of CLIP for end to end video clip retrieval and captioning},
  author={Luo, Huaishao and Ji, Lei and Zhong, Ming and Chen, Yang and Lei, Wen and Duan, Nan and Li, Tianrui},
  journal={Neurocomputing},
  volume={508},
  pages={293--304},
  year={2022},
}