Open-Vocabulary Animal Keypoint Detection with Semantic-feature Matching

📄 arXiv: 2310.05056v4 📥 PDF

作者: Hao Zhang, Lumin Xu, Shenqi Lai, Wenqi Shao, Nanning Zheng, Ping Luo, Yu Qiao, Kaipeng Zhang

分类: cs.CV

发布日期: 2023-10-08 (更新: 2024-10-02)

备注: Accepted by International Journal of Computer Vision

DOI: 10.1007/s11263-024-02126-3

💡 一句话要点

提出开放词汇动物关键点检测方法以解决零-shot检测问题

🎯 匹配领域: 支柱三：空间感知与语义 (Perception & Semantics) 支柱六：视频提取与匹配 (Video Extraction)

关键词: 关键点检测 零-shot学习 开放词汇 语义特征匹配 视觉语言模型 动物识别 深度学习

📋 核心要点

  1. 现有的动物关键点检测方法依赖大量人工标注，限制了其在多物种和关键点类别中的应用。
  2. 本文提出了开放词汇关键点检测任务，利用文本提示实现零-shot关键点检测，减少对人工标注的依赖。
  3. 实验结果显示，KDSM框架在OVKD任务中显著超越基线，零-shot表现与最先进的少量样本方法相当。

📝 摘要（中文）

当前基于图像的动物关键点检测方法通常分为全监督和少量样本类无关方法。全监督方法依赖繁琐的人工标注，限制了关键点检测的扩展性；而少量样本方法仍需参考图像进行测试。为实现零-shot关键点检测，本文提出开放词汇关键点检测（OVKD）任务，利用文本提示识别任意物种的关键点。我们开发了名为KDSM的框架，结合视觉和语言模型，增强了框架的通用性和性能。实验表明，KDSM在OVKD任务中显著优于基线方法，且在零-shot情况下的表现与最先进的少量样本方法相当。

🔬 方法详解

问题定义：本文旨在解决现有动物关键点检测方法对人工标注的高度依赖，限制了其在多物种和关键点类别中的应用。现有方法在扩展性和适应性方面存在显著不足。

核心思路：提出开放词汇关键点检测（OVKD）任务，利用文本提示来识别任意物种的关键点，从而实现零-shot检测，减少对标注数据的需求。

技术框架：KDSM框架结合了视觉和语言模型，通过语义特征匹配实现关键点检测。主要模块包括领域分布矩阵匹配（DDMM）和视觉关键点关系意识（VKRA）模块，增强了模型的通用性和性能。

关键创新：KDSM框架的核心创新在于将视觉特征与语言特征相结合，通过语义特征匹配实现零-shot关键点检测，这一设计与传统方法的依赖标注数据形成鲜明对比。

关键设计：在模型设计中，采用了特定的损失函数和网络结构，以优化关键点检测的准确性和效率，确保在零-shot情况下仍能保持良好的性能。具体的参数设置和模块设计细节将在后续部分详细讨论。

🖼️ 关键图片

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📊 实验亮点

实验结果表明，KDSM框架在OVKD任务中显著优于基线方法，零-shot情况下的检测性能与最先进的少量样本方法相当，展示了在多物种关键点检测中的强大能力。具体性能数据将在论文中详细列出。

🎯 应用场景

该研究的潜在应用领域包括动物行为分析、生态监测和生物多样性研究等。通过实现零-shot关键点检测，研究可以在缺乏大量标注数据的情况下，快速适应新物种的关键点检测需求，具有重要的实际价值和广泛的应用前景。

📄 摘要（原文）

Current image-based keypoint detection methods for animal (including human) bodies and faces are generally divided into full-supervised and few-shot class-agnostic approaches. The former typically relies on laborious and time-consuming manual annotations, posing considerable challenges in expanding keypoint detection to a broader range of keypoint categories and animal species. The latter, though less dependent on extensive manual input, still requires necessary support images with annotation for reference during testing. To realize zero-shot keypoint detection without any prior annotation, we introduce the Open-Vocabulary Keypoint Detection (OVKD) task, which is innovatively designed to use text prompts for identifying arbitrary keypoints across any species. In pursuit of this goal, we have developed a novel framework named Open-Vocabulary Keypoint Detection with Semantic-feature Matching (KDSM). This framework synergistically combines vision and language models, creating an interplay between language features and local keypoint visual features. KDSM enhances its capabilities by integrating Domain Distribution Matrix Matching (DDMM) and other special modules, such as the Vision-Keypoint Relational Awareness (VKRA) module, improving the framework's generalizability and overall performance.Our comprehensive experiments demonstrate that KDSM significantly outperforms the baseline in terms of performance and achieves remarkable success in the OVKD task.Impressively, our method, operating in a zero-shot fashion, still yields results comparable to state-of-the-art few-shot species class-agnostic keypoint detection methods.We will make the source code publicly accessible.