Papers › ViLA: Efficient Video-Language Alignment for Video Question Answering
ViLA: Efficient Video-Language Alignment for Video Question Answering
Xijun Wang, Junbang Liang, Chun-Kai Wang, Kenan Deng, Yu Lou, Ming Lin, Shan Yang
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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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Video Question Answering | NExT-QA | ViLA (3B) | Accuracy | 75.6 | #22 of 47 | Archive leaderboard | report |
| Video Question Answering | NExT-QA | ViLA (3B, 4 frames) | Accuracy | 74.4 | #25 of 47 | Archive leaderboard | report |
| Video Question Answering | NExT-QA (Efficient) | ViLA (3B, 4 frames) | 1:1 Accuracy | 74.4 | #1 of 2 | Archive leaderboard | report |
| Video Question Answering | STAR Benchmark | VLAP (4 frames) | Average Accuracy | 67.1 | #1 of 17 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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