用Mamba设计实时2D关键点检测模型，速度快精度高。
MamKPD: A Simple Mamba Baseline for Real-Time 2D Keypoint Detection
- 用轻量上下文模块建模块间依赖，结合Mamba全局建模
- 在COCO上达77.3% AP，速度1492 FPS，参数量少85%
- 适合需要高速高精度关键点检测的场景
实时2D关键点检测在计算机视觉中至关重要。尽管基于CNN和Transformer的方法取得突破，但常难以兼顾性能与实时性。本文提出首个高效且有效的基于Mamba的姿态估计框架MamKPD。传统Mamba模块在块间信息交互有限，为此我们设计轻量级上下文建模模块（CMM），利用深度卷积建模块间依赖，并通过线性层提取每块内的姿态线索。结合Mamba对所有块的全局建模，MamKPD能有效提取实例姿态信息。我们在人体与动物姿态估计数据集上进行大量实验验证其有效性。MamKPD-L在COCO数据集上达到77.3% AP，NVIDIA GTX 4090上实现1492 FPS。同时在MPII上达到领先结果，在AP-10K上表现竞争力，相比ViTPose减少85%参数量。
原文摘要 · Abstract (English)
Real-time 2D keypoint detection plays an essential role in computer vision. Although CNN-based and Transformer-based methods have achieved breakthrough progress, they often fail to deliver superior performance and real-time speed. This paper introduces MamKPD, the first efficient yet effective mamba-based pose estimation framework for 2D keypoint detection. The conventional Mamba module exhibits limited information interaction between patches. To address this, we propose a lightweight contextual modeling module (CMM) that uses depth-wise convolutions to model inter-patch dependencies and linear layers to distill the pose cues within each patch. Subsequently, by combining Mamba for global modeling across all patches, MamKPD effectively extracts instances' pose information. We conduct extensive experiments on human and animal pose estimation datasets to validate the effectiveness of MamKPD. Our MamKPD-L achieves 77.3% AP on the COCO dataset with 1492 FPS on an NVIDIA GTX 4090 GPU. Moreover, MamKPD achieves state-of-the-art results on the MPII dataset and competitive results on the AP-10K dataset while saving 85% of the parameters compared to ViTPose. Our project page is available at https://mamkpd.github.io/.
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