@inproceedings{
yang2024kptllm,
title={Kpt{LLM}: Unveiling the Power of Large Language Model for Keypoint Comprehension},
author={Jie Yang and Wang ZENG and Sheng Jin and Lumin Xu and Wentao Liu and Chen Qian and Ruimao Zhang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=gwd3MQufGP}
}
Recent advancements in Multimodal Large Language Models (MLLMs) have greatly improved their abilities in image understanding. However, these models often struggle with grasping pixel-level semantic details, e.g., the keypoints of an object. To bridge this gap, we introduce the novel challenge of Semantic Keypoint Comprehension, which aims to comprehend keypoints across different task scenarios, including keypoint semantic understanding, visual prompt-based keypoint detection, and textual prompt-based keypoint detection. Moreover, we introduce KptLLM, a unified multimodal model that utilizes an identify-then-detect strategy to effectively address these challenges. KptLLM underscores the initial discernment of semantics in keypoints, followed by the precise determination of their positions through a chain-of-thought process. With several carefully designed modules, KptLLM adeptly handles various modality inputs, facilitating the interpretation of both semantic contents and keypoint locations. Our extensive experiments demonstrate KptLLM's superiority in various keypoint detection benchmarks and its unique semantic capabilities in interpreting keypoints.
KptLLM: Unveiling the Power of Large Language Model for Keypoint Comprehension
Jie Yang Wang ZENG Sheng Jin Lumin Xu Wentao Liu Chen Qian Ruimao Zhang