Summary of Multi-granularity Contrastive Cross-modal Collaborative Generation For End-to-end Long-term Video Question Answering, by Ting Yu et al.
Multi-granularity Contrastive Cross-modal Collaborative Generation for End-to-End Long-term Video Question Answering
by Ting Yu, Kunhao Fu, Jian Zhang, Qingming Huang, Jun Yu
First submitted to arxiv on: 12 Oct 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 an end-to-end solution for long-term video question answering (VideoQA), a challenging task that requires comprehensive cross-modal reasoning to generate precise answers. The authors propose the Multi-granularity Contrastive cross-modal collaborative Generation (MCG) model, which includes Joint Unimodal Modeling (JUM) and Multi-granularity Contrastive Learning (MCL) for deriving discriminative representations. The MCG model also employs a Cross-modal Collaborative Generation (CCG) module to reformulate VideoQA as a generative task, enabling the model to perform high-semantic fusion and generation. Experimental results on six publicly available datasets demonstrate the superiority of the proposed method. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about making computers better at understanding videos and answering questions about what’s happening in them. The authors created a new way for computers to learn from videos and questions, which helps them get better answers. This is important because it can help us build more helpful AI systems that can understand and respond to human language. |
Keywords
» Artificial intelligence » Question answering
