Summary of Causal Understanding For Video Question Answering, by Bhanu Prakash Reddy Guda et al.
Causal Understanding For Video Question Answering
by Bhanu Prakash Reddy Guda, Tanmay Kulkarni, Adithya Sampath, Swarnashree Mysore Sathyendra
First submitted to arxiv on: 23 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 This research paper proposes novel solutions to improve Video Question Answering (VQA) models’ ability to reason over multiple frames and understand object interactions in videos like NExT-QA. Building upon previous approaches that leveraged sub-sampled information or causal intervention techniques, the authors identify limitations and introduce four novel directions for improvement on the NExT-QA dataset. These include smartly sampling frames, explicitly encoding actions, and creating interventions to challenge model understanding. The proposed approaches achieve state-of-the-art results (+6.3% for single-frame and +1.1% for complete-video) on the NExT-QA dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps VQA models get better at understanding videos by making them smarter. Researchers looked at how previous methods worked and found ways to make them even more effective. They tried four new approaches to improve how well the models can answer questions about what’s happening in a video. This led to big improvements, with results that are now the best they’ve ever been! |
Keywords
» Artificial intelligence » Question answering
