most citedWeakly-Supervised Surgical Phase Recognition

1 citations · 2 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV2024

Anchored Diffusion for Video Face Reenactment

Idan Kligvasser, Regev Cohen, George Leifman +2

Video generation has drawn significant interest recently, pushing the development of large-scale models capable of producing realistic videos with coherent motion. Due to memory co…

cs.LG2024

Molecular Diffusion Models with Virtual Receptors

Matan Halfon, Eyal Rozenberg, Ehud Rivlin +1

Machine learning approaches to Structure-Based Drug Design (SBDD) have proven quite fertile over the last few years. In particular, diffusion-based approaches to SBDD have shown gr…

eess.IV2024

Predicting Generalization of AI Colonoscopy Models to Unseen Data

Joel Shor, Carson McNeil, Yotam Intrator +15

: Generalizability of AI colonoscopy algorithms is important for wider adoption in clinical practice. However, current techniques for evaluating performance on…

cs.CL20241 cited

Breaking the Language Barrier: Can Direct Inference Outperform Pre-Translation in Multilingual LLM Applications?

Yotam Intrator, Matan Halfon, Roman Goldenberg +5

Large language models hold significant promise in multilingual applications. However, inherent biases stemming from predominantly English-centric pre-training have led to the wides…

cs.CV20231 cited

Weakly-Supervised Surgical Phase Recognition

Roy Hirsch, Regev Cohen, Mathilde Caron +3

A key element of computer-assisted surgery systems is phase recognition of surgical videos. Existing phase recognition algorithms require frame-wise annotation of a large number of…

cs.CV2023

Weakly-supervised Representation Learning for Video Alignment and Analysis

Guy Bar-Shalom, George Leifman, Michael Elad +1

Many tasks in video analysis and understanding boil down to the need for frame-based feature learning, aiming to encapsulate the relevant visual content so as to enable simpler and…