Summary of Tv-trees: Multimodal Entailment Trees For Neuro-symbolic Video Reasoning, by Kate Sanders et al.
TV-TREES: Multimodal Entailment Trees for Neuro-Symbolic Video Reasoning
by Kate Sanders, Nathaniel Weir, Benjamin Van Durme
First submitted to arxiv on: 29 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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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 proposed TV-TREES model is designed to address the limitations of current video-language models by introducing a multimodal entailment tree generator. This approach enables interpretable joint-modality reasoning, allowing for the generation of trees that prove question-answer pairs using simple text-video evidence and higher-level conclusions. The paper introduces the task of multimodal entailment tree generation to evaluate the quality of reasoning and demonstrates state-of-the-art performance on the TVQA benchmark in zero-shot settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary TV-TREES is a new way for computers to understand videos by creating connections between what’s happening in the video and the words that describe it. This helps machines reason better about complex content, making them more accurate and easier to understand. The idea behind this approach is that humans can learn from the connections created by TV-TREES, which can lead to better decision-making. |
Keywords
» Artificial intelligence » Zero shot
