163 citations · 200 across the 8 of their papers we have counts for
10 papers
Rethinking Math Reasoning Evaluation: A Robust LLM-as-a-Judge Framework Beyond Symbolic Rigidity
Erez Yosef, Oron Anschel, Shunit Haviv Hakimi +4
Recent advancements in large language models have led to significant improvements across various tasks, including mathematical reasoning, which is used to assess models' intelligen…
Group-Aware Reinforcement Learning for Output Diversity in Large Language Models
Oron Anschel, Alon Shoshan, Adam Botach +7
Large Language Models (LLMs) often suffer from mode collapse, repeatedly generating the same few completions even when many valid answers exist, limiting their diversity across a w…
LV-MAE: Learning Long Video Representations through Masked-Embedding Autoencoders
Ilan Naiman, Emanuel Ben-Baruch, Oron Anschel +4
In this work, we introduce long-video masked-embedding autoencoders (LV-MAE), a self-supervised learning framework for long video representation. Our approach treats short- and lon…
GLASS: Global to Local Attention for Scene-Text Spotting
Roi Ronen, Shahar Tsiper, Oron Anschel +3
In recent years, the dominant paradigm for text spotting is to combine the tasks of text detection and recognition into a single end-to-end framework. Under this paradigm, both tas…
Learning Multimodal Affinities for Textual Editing in Images
Or Perel, Oron Anschel, Omri Ben-Eliezer +2
Nowadays, as cameras are rapidly adopted in our daily routine, images of documents are becoming both abundant and prevalent. Unlike natural images that capture physical objects, do…
On Calibration of Scene-Text Recognition Models
Ron Slossberg, Oron Anschel, Amir Markovitz +6
In this work, we study the problem of word-level confidence calibration for scene-text recognition (STR). Although the topic of confidence calibration has been an active research a…
Sequence-to-Sequence Contrastive Learning for Text Recognition
Aviad Aberdam, Ron Litman, Shahar Tsiper +5
We propose a framework for sequence-to-sequence contrastive learning (SeqCLR) of visual representations, which we apply to text recognition. To account for the sequence-to-sequence…
SCATTER: Selective Context Attentional Scene Text Recognizer
Ron Litman, Oron Anschel, Shahar Tsiper +3
Scene Text Recognition (STR), the task of recognizing text against complex image backgrounds, is an active area of research. Current state-of-the-art (SOTA) methods still struggle…
Model-based Adversarial Imitation Learning
Nir Baram, Oron Anschel, Shie Mannor
Generative adversarial learning is a popular new approach to training generative models which has been proven successful for other related problems as well. The general idea is to…
Averaged-DQN: Variance Reduction and Stabilization for Deep Reinforcement Learning
Oron Anschel, Nir Baram, Nahum Shimkin
Instability and variability of Deep Reinforcement Learning (DRL) algorithms tend to adversely affect their performance. Averaged-DQN is a simple extension to the DQN algorithm, bas…