32 citations · 104 across the 19 of their papers we have counts for
6 papers · 1 filter
VisionMX: Unlocking Microscaling Post-Training Quantization for Vision Models
Elad Dror Cohen, Ofir Gordon, Lior Dikstein +2
Microscaling (MX) formats are emerging as a hardware-supported approach to efficient training and inference. They combine low-precision elements with shared block scales, but their…
Lay-A-Scene: Personalized 3D Object Arrangement Using Text-to-Image Priors
Ohad Rahamim, Hilit Segev, Idan Achituve +3
Generating 3D visual scenes is at the forefront of visual generative AI, but current 3D generation techniques struggle with generating scenes with multiple high-resolution objects.…
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…
GD-VDM: Generated Depth for better Diffusion-based Video Generation
Ariel Lapid, Idan Achituve, Lior Bracha +1
The field of generative models has recently witnessed significant progress, with diffusion models showing remarkable performance in image generation. In light of this success, ther…
Auxiliary Learning by Implicit Differentiation
Aviv Navon, Idan Achituve, Haggai Maron +2
Training neural networks with auxiliary tasks is a common practice for improving the performance on a main task of interest. Two main challenges arise in this multi-task learning s…
Self-Supervised Learning for Domain Adaptation on Point-Clouds
Idan Achituve, Haggai Maron, Gal Chechik
Self-supervised learning (SSL) is a technique for learning useful representations from unlabeled data. It has been applied effectively to domain adaptation (DA) on images and video…