Summary of Causalchaos! Dataset For Comprehensive Causal Action Question Answering Over Longer Causal Chains Grounded in Dynamic Visual Scenes, by Paritosh Parmar et al.
CausalChaos! Dataset for Comprehensive Causal Action Question Answering Over Longer Causal Chains Grounded in Dynamic Visual Scenes
by Paritosh Parmar, Eric Peh, Ruirui Chen, Ting En Lam, Yuhan Chen, Elston Tan, Basura Fernando
First submitted to arxiv on: 1 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
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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 proposes a novel dataset called CausalChaos! for causal video question answering (QA), which addresses the lack of depth in causal reasoning in existing datasets. The dataset is built upon the “Tom and Jerry” cartoon series, utilizing its unique properties to create challenging causal relationships between events. The questions involve complex causal chains that require models to solve more sophisticated causal relationships. While current models perform well, there is still room for improvement, especially on open-ended answers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Causal video question answering is an exciting area of research! This paper creates a new dataset called CausalChaos! that uses cartoons to help machines better understand cause-and-effect relationships. The cartoon “Tom and Jerry” series is perfect for this task because its animations are designed to show clear connections between events, making it easier for computers to learn from. The questions in the dataset ask about these relationships, which helps models get better at solving complex problems. While current AI systems do well, there’s still room for improvement, especially when answering open-ended questions. |
Keywords
» Artificial intelligence » Question answering
