Edge Weight Prediction For Category-Agnostic Pose Estimation
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.
6745ee44b7b160fc58aae8f0
Or Hirschorn
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2024-11-26T21:20:29.100Z
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Or Hirschschorn
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orhir
6745ee44b7b160fc58aae8f1
Shai Avidan
Edge Weight Prediction For Category-Agnostic Pose Estimation
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.
EdgeCape, a framework that learns dynamic graph edge weights and integrates Markovian Structural Bias, improves category-agnostic keypoint localization across diverse categories.
pose graph keypoints dynamic graph edge weights Markovian Structural Bias self-attention global spatial dependencies