Summary of B-vllm: a Vision Large Language Model with Balanced Spatio-temporal Tokens, by Zhuqiang Lu et al.
B-VLLM: A Vision Large Language Model with Balanced Spatio-Temporal Tokens
by Zhuqiang Lu, Zhenfei Yin, Mengwei He, Zhihui Wang, Zicheng Liu, Zhiyong Wang, Kun Hu
First submitted to arxiv on: 13 Dec 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 paper presents a novel framework, Balanced-VLLM (B-VLLM), to improve the performance of Vision Large Language Models (VLLMs) in video understanding. B-VLLM aims to effectively leverage spatio-temporal cues while restricting the number of visual tokens within the VLLM context window length. The method involves a text-conditioned adaptive frame selection module, temporal frame token merging technique, spatial token sampling module, and optional spatial token merging strategy. Experimental results show that B-VLLM achieves superior performance on various video understanding benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary B-VLLM is a new way to make computers better at understanding videos. Currently, these computers have trouble dealing with long videos because they get overwhelmed by too many visual details. The researchers came up with a solution that helps the computer focus on the most important parts of the video while still capturing all the important details. This makes the computer much better at recognizing what’s happening in the video. The new method is tested and shown to be very effective. |
Keywords
» Artificial intelligence » Context window » Token
