LiteVL: Efficient Video-Language Learning with Enhanced Spatial-Temporal Modeling

Dongsheng Chen, Chaofan Tao, Lu Hou, Lifeng Shang, Xin Jiang, Qun Liu


Abstract
Recent large-scale video-language pre-trained models have shown appealing performance on various downstream tasks. However, the pre-training process is computationally expensive due to the requirement of millions of video-text pairs and the redundant data structure of each video. To mitigate these problems, we propose LiteVL, which adapts a pre-trained image-language model BLIP into a video-text model directly on downstream tasks, without heavy pre-training. To enhance the temporal modeling lacking in the image-language model, we propose to add temporal attention modules in the image encoder of BLIP with dynamic temporal scaling. Besides the model-wise adaptation, we also propose a non-parametric pooling mechanism to adaptively reweight the fine-grained video embedding conditioned on the text. Experimental results on text-video retrieval and video question answering show that the proposed LiteVL even outperforms previous video-language pre-trained models by a clear margin, though without any video-language pre-training.
Anthology ID:
2022.emnlp-main.545
Volume:
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Editors:
Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
7985–7997
Language:
URL:
https://aclanthology.org/2022.emnlp-main.545
DOI:
10.18653/v1/2022.emnlp-main.545
Bibkey:
Cite (ACL):
Dongsheng Chen, Chaofan Tao, Lu Hou, Lifeng Shang, Xin Jiang, and Qun Liu. 2022. LiteVL: Efficient Video-Language Learning with Enhanced Spatial-Temporal Modeling. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 7985–7997, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
LiteVL: Efficient Video-Language Learning with Enhanced Spatial-Temporal Modeling (Chen et al., EMNLP 2022)
Copy Citation:
PDF:
https://preview.aclanthology.org/improve-issue-templates/2022.emnlp-main.545.pdf