Summary of Defeasible Visual Entailment: Benchmark, Evaluator, and Reward-driven Optimization, by Yue Zhang et al.
Defeasible Visual Entailment: Benchmark, Evaluator, and Reward-Driven Optimization
by Yue Zhang, Liqiang Jing, Vibhav Gogate
First submitted to arxiv on: 19 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 introduces Defeasible Visual Entailment (DVE), a task that enables refinement of initial interpretations in visual entailment tasks. DVE allows for modification of the entailment relationship between an image premise and a text hypothesis based on updates, which is particularly important for applications like detecting misleading information in images, enhancing visual question answering, and refining decision-making processes in autonomous systems. The paper proposes a novel inference-aware evaluator that captures changes in entailment strength induced by updates using pairwise contrastive learning and categorical information learning. Additionally, it introduces a reward-driven update optimization method to enhance the quality of updates generated by multimodal models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about making computers better at understanding pictures and what they mean. It wants to help computers be more accurate when trying to figure out if something in an image is true or not. The new way it’s proposing lets computers update their understanding based on new information, which can be really helpful for things like catching fake news or helping self-driving cars make better decisions. |
Keywords
» Artificial intelligence » Inference » Optimization » Question answering
