Edge Weight Prediction For Category-Agnostic Pose Estimation
Authors: name: Or Hirschorn, name: Shai Avidan

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.