2k citations · 2k across the 10 of their papers we have counts for
11 papers
Adaptive Latent Capacity for World Models
Idan Achituve, Lior Dikstein, Idit Diamant +2
We introduce Adaptive LeWorldModel (ALeWM), a world model based on a joint-embedding predictive architecture (JEPA) that learns to concentrate predictive information in compact pre…
Low Light Image Enhancement Challenge at NTIRE 2026
George Ciubotariu, Sharif S M A, Abdur Rehman +90
This paper presents a comprehensive review of the NTIRE 2026 Low Light Image Enhancement Challenge, highlighting the proposed solutions and final results. The objective of this cha…
Inverse Problem Sampling in Latent Space Using Sequential Monte Carlo
Idan Achituve, Hai Victor Habi, Amir Rosenfeld +3
In image processing, solving inverse problems is the task of finding plausible reconstructions of an image that was corrupted by some (usually known) degradation operator. Commonly…
Efficient Image Restoration via Latent Consistency Flow Matching
Elad Cohen, Idan Achituve, Idit Diamant +2
Recent advances in generative image restoration (IR) have demonstrated impressive results. However, these methods are hindered by their substantial size and computational demands,…
Bayesian Uncertainty for Gradient Aggregation in Multi-Task Learning
Idan Achituve, Idit Diamant, Arnon Netzer +2
As machine learning becomes more prominent there is a growing demand to perform several inference tasks in parallel. Running a dedicated model for each task is computationally expe…
De-Confusing Pseudo-Labels in Source-Free Domain Adaptation
Idit Diamant, Amir Rosenfeld, Idan Achituve +2
Source-free domain adaptation aims to adapt a source-trained model to an unlabeled target domain without access to the source data. It has attracted growing attention in recent yea…
Reconciling a Centroid-Hypothesis Conflict in Source-Free Domain Adaptation
Idit Diamant, Roy H. Jennings, Oranit Dror +2
Source-free domain adaptation (SFDA) aims to transfer knowledge learned from a source domain to an unlabeled target domain, where the source data is unavailable during adaptation.…
HPTQ: Hardware-Friendly Post Training Quantization
Hai Victor Habi, Reuven Peretz, Elad Cohen +5
Neural network quantization enables the deployment of models on edge devices. An essential requirement for their hardware efficiency is that the quantizers are hardware-friendly: u…
Multi-View Image-to-Image Translation Supervised by 3D Pose
Idit Diamant, Oranit Dror, Hai Victor Habi +1
We address the task of multi-view image-to-image translation for person image generation. The goal is to synthesize photo-realistic multi-view images with pose-consistency across a…
GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification
Maayan Frid-Adar, Idit Diamant, Eyal Klang +3
Deep learning methods, and in particular convolutional neural networks (CNNs), have led to an enormous breakthrough in a wide range of computer vision tasks, primarily by using lar…
Modeling the Intra-class Variability for Liver Lesion Detection using a Multi-class Patch-based CNN
Maayan Frid-Adar, Idit Diamant, Eyal Klang +3
Automatic detection of liver lesions in CT images poses a great challenge for researchers. In this work we present a deep learning approach that models explicitly the variability w…