1 citations · 1 across the 3 of their papers we have counts for
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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…
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.…
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…