ViLA: Efficient Video-Language Alignment for Video Question Answering

• URL: http://arxiv.org/abs/2312.08367v4
• Date: Tue, 01 Oct 2024 10:11:14 GMT
• Title: ViLA: Efficient Video-Language Alignment for Video Question Answering
• Authors: Xijun Wang, Junbang Liang, Chun-Kai Wang, Kenan Deng, Yu Lou, Ming Lin, Shan Yang,
• Abstract summary: Our ViLA network addresses both efficient frame sampling and effective cross-modal alignment. Our ViLA network outperforms the state-of-the-art methods on the video question-answering benchmarks.
• Score: 22.972518862771697
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this work, we propose an efficient Video-Language Alignment (ViLA) network. Our ViLA model addresses both efficient frame sampling and effective cross-modal alignment in a unified way. In our ViLA network, we design a new learnable text-guided Frame-Prompter together with a new cross-modal distillation (QFormer-Distiller) module. Pre-trained large image-language models have shown promising results on problems such as visual question answering (VQA). However, how to efficiently and effectively sample video frames when adapting pre-trained large image-language model to video-language alignment is still the major challenge. Compared with prior work, our ViLA model demonstrates the capability of selecting key frames with critical contents, thus improving the video-language alignment accuracy while reducing the inference latency +3.3% on NExT-QA Temporal with 3.0X speed up). Overall, our ViLA network outperforms the state-of-the-art methods on the video question-answering benchmarks: +4.6% on STAR Interaction, +2.2% on STAR average with 3.0X speed up, ours 2-frames out-perform SeViLA 4-frames on the VLEP dataset with 4.2X speed-up. The code will be available at https://github.com/xijun-cs/ViLA.

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