Summary of Video-ccam: Enhancing Video-language Understanding with Causal Cross-attention Masks For Short and Long Videos, by Jiajun Fei et al.
Video-CCAM: Enhancing Video-Language Understanding with Causal Cross-Attention Masks for Short and Long Videos
by Jiajun Fei, Dian Li, Zhidong Deng, Zekun Wang, Gang Liu, Hui Wang
First submitted to arxiv on: 26 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The abstract proposes a novel approach to improving the performance of large language models (LLMs) in processing videos. The existing methods either downsample visual features or extend the LLM context size, which risks losing high-resolution information or slowing down inference speed. To address this limitation, the authors apply cross-attention layers in the intermediate projector between the visual encoder and the LLM. Additionally, they introduce causal cross-attention masks (CCAMs) within the cross-attention layers to ensure that the model is sensitive to temporal order. The proposed Video-MLLM, named Video-CCAM, is trained in a two-stage fashion: feature alignment and visual instruction tuning. The authors develop several Video-CCAM models based on LLMs of different sizes (4B, 9B, and 14B) and evaluate their performance on various video benchmarks. The results show that Video-CCAM outperforms existing methods in many cases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The abstract proposes a new way to help computers understand videos better. Currently, there are ways to make language models work with videos, but they have some problems. For example, some methods might lose important details or take too long to process. To solve this, the researchers added special attention layers in their model that can look at both the video and text parts simultaneously. They also introduced a new type of mask to help the model understand the order of events in the video. The resulting Video-CCAM model is trained on images and short videos, but it can still do well when working with longer videos. In fact, it outperforms other models on many tests. |
Keywords
» Artificial intelligence » Alignment » Attention » Cross attention » Encoder » Inference » Instruction tuning » Mask
