Summary of Encoding and Controlling Global Semantics For Long-form Video Question Answering, by Thong Thanh Nguyen et al.
Encoding and Controlling Global Semantics for Long-form Video Question Answering
by Thong Thanh Nguyen, Zhiyuan Hu, Xiaobao Wu, Cong-Duy T Nguyen, See-Kiong Ng, Anh Tuan Luu
First submitted to arxiv on: 30 May 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 A novel approach to video question answering (videoQA) systems is proposed, addressing the limitation of previous methods in adapting frame and region selection for long videos. A state space layer (SSL) is integrated into a multi-modal Transformer to efficiently incorporate global semantics from the video sequence, mitigating information loss. The SSL includes a gating unit controlling the flow of global semantics into visual representations. Additionally, a cross-modal compositional congruence (C^3) objective is introduced to align global semantics with questions. Two new benchmarks, Ego-QA and MAD-QA, are constructed featuring long videos (17.5 minutes and 1.9 hours), demonstrating the superiority of the framework on these datasets as well as existing ones. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Long video question answering systems are important for building effective videoQA systems. Previous methods tried to save computations by selecting frames and regions from long videos, but this didn’t work very well because it lost important information about the whole video. A new way is proposed to keep this information using a “state space layer” in a special kind of computer program called a Transformer. This helps to make decisions based on the entire video, not just parts of it. The researchers also added an extra step to make sure the global understanding of the video matches with the question being asked. Two new tests were created to see how well this method works, and it performed better than other methods. |
Keywords
» Artificial intelligence » Multi modal » Question answering » Semantics » Transformer
