Summary of Self-explainable Affordance Learning with Embodied Caption, by Zhipeng Zhang et al.
Self-Explainable Affordance Learning with Embodied Caption
by Zhipeng Zhang, Zhimin Wei, Guolei Sun, Peng Wang, Luc Van Gool
First submitted to arxiv on: 8 Apr 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 This paper proposes Self-Explainable Affordance learning (SEA) for robots to better understand visual affordances, enabling them to articulate their intentions and rectify errors through embodied captions. The authors introduce a pioneering dataset and metrics tailored for this task, integrating images, heatmaps, and captions. They also propose a novel model that combines affordance grounding with self-explanation in an efficient manner. The approach is shown to be effective through extensive quantitative and qualitative experiments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper aims to improve robots’ understanding of visual affordances by introducing Self-Explainable Affordance learning (SEA) using embodied captions. This innovation allows robots to describe their intentions, helping them correct mistakes and interact more effectively with the environment. The authors create a new dataset and metrics for this task, which integrates images, heatmaps, and captions. |
Keywords
» Artificial intelligence » Grounding
