Or Hirschorn

Or Hirschorn, Omer Sela, Inbar Huberman-Spiegelglas, Netalee Efrat, Eli Alshan, Ianir Ideses, Frederic Devernay, Frederic Devernay, Yochai Zvik, Lior Fritz

Or Hirschorn*, Leo Segre*, Shai Avidan

Or Hirschorn, Shai Avidan

We perform category-agnostic pose estimation by predicting structural edge weights to improve keypoint localization accuracy.

Matan Rusanovsky, Or Hirschorn, Shai Avidan

We perform open-vocabulary pose estimation. Given text prompt describing object parts, our method localizes these keypoints in an input image.

Shir Bar, Or Hirschorn, Roi Holzman, Shai Avidan

We present a pipeline leveraging anomaly detection with Graph Convolutional Networks to efficiently sample rare animal behaviors from large-scale, unlabeled datasets, significantly reducing annotation costs while improving classifier performance, particularly for behaviors as rare as 0.02% of the data.

Or Hirschorn, Shai Avidan

Pose Anything - given only one example image and skeleton, our method can perform pose estimation on unseen categories.

Or Hirschorn*, Amir Jevnisek*, Shai Avidan

We propose Optimize & Reduce (O&R), a top-down approach to vectorization that is both fast and domain-agnostic. O&R aims to attain a compact representation of input images by iteratively optimizing Bézier curve parameters and significantly reducing the number of shapes, using a devised importance measure.

Or Hirschorn, Shai Avidan

We perform video anomaly detection, distilling the problem to anomaly detection of human pose. We leverage the highly compact pose representation in a normalizing flows framework, which we extend to tackle the unique characteristics of spatio-temporal pose data and show its advantages in this use case.

Leo Segre*, Or Hirschorn*, Dvir Ginzburg, Dan Raviv

We use cross-modality adaptation between CT and MRI whole cardiac scans for semantic segmentation.