Or Hirschorn

I am a Ph.D. candidate at the School of Electrical Engineering at Tel-Aviv University, under the joint supervision of Prof. Shai Avidan.

My research interests include machine learning and computer vision. More specifically, I am interested in developing new tools for pose estimation, semantic correspondence and other vector semantic representations.

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Publications

CapeX: Category-Agnostic Pose Estimation from Textual Point Explanation

UNDER REVIEW
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.

arXiv Code

Pose Anything: A Graph-Based Approach for Category-Agnostic Pose Estimation

UNDER REVIEW
Or Hirschorn, Shai Avidan

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

Project Page arXiv Code

Optimize and Reduce: A Top-Down Approach for Image Vectorization

AAAI 2024
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.

Project Page arXiv Code

Normalizing Flows for Human Pose Anomaly Detection

ICCV 2023
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.

arXiv Code

Shape-Consistent Generative Adversarial Networks for Multi-Modal Medical Segmentation Maps

ISBI 2022
Leo Segre*, Or Hirschorn*, Shai Avidan

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

arXiv Code