Summary of Worldafford: Affordance Grounding Based on Natural Language Instructions, by Changmao Chen and Yuren Cong and Zhen Kan
WorldAfford: Affordance Grounding based on Natural Language Instructions
by Changmao Chen, Yuren Cong, Zhen Kan
First submitted to arxiv on: 21 May 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 The proposed paper introduces a novel task called affordance grounding that aims to localize interaction regions for manipulated objects in a scene image based on natural language instructions. The current state-of-the-art approaches primarily support simple action labels as input instructions and struggle to capture complex human objectives, ignoring object context and failing to localize affordance regions of multiple objects in complex scenes. To address this challenge, the authors propose WorldAfford, a new framework that includes an Affordance Reasoning Chain-of-Thought Prompting mechanism to reason about affordance knowledge from language models more precisely and logically. The framework also employs SAM and CLIP to localize objects related to affordance knowledge in the image and identify affordance regions of objects through an affordance region localization module. Extensive experiments are conducted on both the previous AGD20K dataset and a new LLMaFF dataset, demonstrating that WorldAfford achieves state-of-the-art performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new task called affordance grounding, which helps machines understand human instructions and use tools in the environment to accomplish tasks. Currently, most approaches only work with simple action labels and struggle to understand complex human objectives or multiple objects in scenes. The authors propose a new framework called WorldAfford that can reason about affordance knowledge from language models more accurately. They also design a way to localize objects related to affordance knowledge in images and identify the areas where objects can be interacted with. The framework is tested on two datasets, showing that it performs better than previous approaches. |
Keywords
» Artificial intelligence » Grounding » Prompting » Sam
