title: Edge Weight Prediction For Category-Agnostic Pose Estimation
summary: Category-Agnostic Pose Estimation (CAPE) localizes keypoints across diverse
  object categories with a single model, using one or a few annotated support
  images. Recent works have shown that using a pose graph (i.e., treating
  keypoints as nodes in a graph rather than isolated points) helps handle
  occlusions and break symmetry. However, these methods assume a static pose
  graph with equal-weight edges, leading to suboptimal results. We introduce
  EdgeCape, a novel framework that overcomes these limitations by predicting the
  graph's edge weights which optimizes localization. To further leverage
  structural priors, we propose integrating Markovian Structural Bias, which
  modulates the self-attention interaction between nodes based on the number of
  hops between them. We show that this improves the model's ability to capture
  global spatial dependencies. Evaluated on the MP-100 benchmark, which includes
  100 categories and over 20K images, EdgeCape achieves state-of-the-art results
  in the 1-shot setting and leads among similar-sized methods in the 5-shot
  setting, significantly improving keypoint localization accuracy. Our code is
  publicly available.
paper_authors._id: 6745ee44b7b160fc58aae8f0
paper_authors.name: Or Hirschorn
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-11-26T21:20:29.100Z
paper_authors._id: 6318585a70b013d66093c9f5
paper_authors.avatarUrl: /avatars/25a3d2693e7427f2ce9111cc9bc3bf8c.svg
paper_authors.fullname: Or Hirschschorn
paper_authors.type: user
paper_authors.user: orhir
paper_authors._id: 6745ee44b7b160fc58aae8f1
paper_authors.name: Shai Avidan
paper_title: Edge Weight Prediction For Category-Agnostic Pose Estimation
paper_summary: Category-Agnostic Pose Estimation (CAPE) localizes keypoints across diverse
  object categories with a single model, using one or a few annotated support
  images. Recent works have shown that using a pose graph (i.e., treating
  keypoints as nodes in a graph rather than isolated points) helps handle
  occlusions and break symmetry. However, these methods assume a static pose
  graph with equal-weight edges, leading to suboptimal results. We introduce
  EdgeCape, a novel framework that overcomes these limitations by predicting the
  graph's edge weights which optimizes localization. To further leverage
  structural priors, we propose integrating Markovian Structural Bias, which
  modulates the self-attention interaction between nodes based on the number of
  hops between them. We show that this improves the model's ability to capture
  global spatial dependencies. Evaluated on the MP-100 benchmark, which includes
  100 categories and over 20K images, EdgeCape achieves state-of-the-art results
  in the 1-shot setting and leads among similar-sized methods in the 5-shot
  setting, significantly improving keypoint localization accuracy. Our code is
  publicly available.
paper_ai_keywords: pose graph keypoints graph occlusions break symmetry edge weights Markovian Structural Bias self-attention global spatial caching 1-shot setting 5-shot setting keypoint localization accuracy