Summary of Capellm: Support-free Category-agnostic Pose Estimation with Multimodal Large Language Models, by Junho Kim et al.
CapeLLM: Support-Free Category-Agnostic Pose Estimation with Multimodal Large Language Models
by Junho Kim, Hyungjin Chung, Byung-Hoon Kim
First submitted to arxiv on: 11 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 CapeLLM, a novel approach for category-agnostic pose estimation (CAPE) that leverages text-based multimodal large language models (MLLMs). The traditional method relies on support images with annotated keypoints, which can be cumbersome and limited. In contrast, CapeLLM uses query image and detailed text descriptions as input to estimate keypoints. The paper explores the design space of LLM-based CAPE, examining factors such as optimal description choice, neural network architectures, and training strategies. The approach demonstrates superior generalization and robust performance, setting a new state-of-the-art on the MP-100 benchmark in the challenging 1-shot setting. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CapeLLM is a new way to estimate object positions (pose) without needing extra information about specific objects. Right now, we use images with annotated points to do this, which can be tricky and might not work well for all types of objects. The paper shows that using text descriptions instead can be much better. They test different ways of using these text-based models and find the best combination works really well. This means we can accurately estimate object positions without needing extra information. |
Keywords
» Artificial intelligence » 1 shot » Generalization » Neural network » Pose estimation
