Summary of Seqafford: Sequential 3d Affordance Reasoning Via Multimodal Large Language Model, by Chunlin Yu et al.
SeqAfford: Sequential 3D Affordance Reasoning via Multimodal Large Language Model
by Chunlin Yu, Hanqing Wang, Ye Shi, Haoyang Luo, Sibei Yang, Jingyi Yu, Jingya Wang
First submitted to arxiv on: 2 Dec 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 introduces the Sequential 3D Affordance Reasoning task, which extends traditional affordance segmentation paradigms to reason about complex user intentions. The task involves decomposing user instructions into a series of segmentation maps for long-horizon tasks. To tackle this challenge, the authors propose SeqAfford, a model that combines a 3D multi-modal large language model with additional affordance segmentation abilities. SeqAfford incorporates world knowledge and fine-grained affordance grounding in a cohesive framework. The authors also introduce a multi-granular language-point integration module for dense prediction. Experimental evaluations demonstrate that SeqAfford outperforms well-established methods and exhibits open-world generalization with sequential reasoning capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to follow instructions to build something, like a piece of furniture. This paper is about making computers better at understanding human instructions when it comes to manipulating objects in 3D space. Right now, computers are only good at following simple instructions and can’t handle complex tasks that require reasoning. The authors propose a new way for computers to understand instructions by breaking them down into smaller steps and using knowledge of the world to make sense of them. They also introduce a new way for computers to integrate language and 3D data, which helps with this task. The results show that their approach is better than existing methods and can even handle complex tasks that require sequential reasoning. |
Keywords
» Artificial intelligence » Generalization » Grounding » Large language model » Multi modal
