3 citations · 3 across the 4 of their papers we have counts for
5 papers
GLOW-FM: Efficient Generative Low-Light Image Enhancement with One-Step Latent Flow Matching
Ariel Lapid, Yuval Haitman, Reuven Peretz +2
Low-light image enhancement (LLIE) aims to recover well-exposed and perceptually faithful images from severely degraded observations. Recent diffusion-based generative methods achi…
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…
MLoRQ: Bridging Low-Rank and Quantization for Transformer Compression
Ofir Gordon, Ariel Lapid, Elad Cohen +3
Deploying transformer-based neural networks on resource-constrained edge devices presents a significant challenge. This challenge is often addressed through various techniques, suc…
Data Generation for Hardware-Friendly Post-Training Quantization
Lior Dikstein, Ariel Lapid, Arnon Netzer +1
Zero-shot quantization (ZSQ) using synthetic data is a key approach for post-training quantization (PTQ) under privacy and security constraints. However, existing data generation m…
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…