1 citations · 1 across the 9 of their papers we have counts for
11 papers
Person Identification from Contextual Motion
Igor Kviatkovsky, Ehud Rivlin, Ilan Shimshoni
We consider the problem of identifying people based on their motion styles. We present a generative model describing the action instance creation process and derive a probabilistic…
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…
Scene-VLM: Multimodal Video Scene Segmentation via Vision-Language Models
Nimrod Berman, Adam Botach, Emanuel Ben-Baruch +5
Segmenting long-form videos into semantically coherent scenes is a fundamental task in large-scale video understanding. Existing encoder-based methods are limited by visual-centric…
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…
Distilling the Knowledge in Data Pruning
Emanuel Ben-Baruch, Adam Botach, Igor Kviatkovsky +2
With the increasing size of datasets used for training neural networks, data pruning becomes an attractive field of research. However, most current data pruning algorithms are limi…
FPGAN-Control: A Controllable Fingerprint Generator for Training with Synthetic Data
Alon Shoshan, Nadav Bhonker, Emanuel Ben Baruch +5
Training fingerprint recognition models using synthetic data has recently gained increased attention in the biometric community as it alleviates the dependency on sensitive persona…
Asymmetric Image Retrieval with Cross Model Compatible Ensembles
Ori Linial, Alon Shoshan, Nadav Bhonker +4
The asymmetrical retrieval setting is a well suited solution for resource constrained applications such as face recognition and image retrieval. In this setting, a large model is u…
Synthetic Data for Model Selection
Alon Shoshan, Nadav Bhonker, Igor Kviatkovsky +2
Recent breakthroughs in synthetic data generation approaches made it possible to produce highly photorealistic images which are hardly distinguishable from real ones. Furthermore,…
GAN-Control: Explicitly Controllable GANs
Alon Shoshan, Nadav Bhonker, Igor Kviatkovsky +1
We present a framework for training GANs with explicit control over generated images. We are able to control the generated image by settings exact attributes such as age, pose, exp…
From Real to Synthetic and Back: Synthesizing Training Data for Multi-Person Scene Understanding
Igor Kviatkovsky, Nadav Bhonker, Gerard Medioni
We present a method for synthesizing naturally looking images of multiple people interacting in a specific scenario. These images benefit from the advantages of synthetic data: bei…