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

• URL: http://arxiv.org/abs/2210.11929v1
• Date: Fri, 21 Oct 2022 13:03:49 GMT
• Title: LiteVL: Efficient Video-Language Learning with Enhanced Spatial-Temporal Modeling
• Authors: Dongsheng Chen, Chaofan Tao, Lu Hou, Lifeng Shang, Xin Jiang, Qun Liu
• Abstract summary: We propose LiteVL, which adapts a pre-trained image-language model BLIP into a video-text model directly on downstream tasks. We also propose a non-parametric pooling mechanism to adaptively reweight the fine-grained video embedding conditioned on the text.
• Score: 48.283659682112926
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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