most citedCascadedGaze: Efficiency in Global Context Extraction for Image Restoration

9 citations · 9 across the 6 of their papers we have counts for

collaborators

6 papers

cs.LG2025

GLASS: Global-Local Aggregation for Inference-time Sparsification of LLMs

Amirmohsen Sattarifard, Sepehr Lavasani, Kunlin Zhang +5

Inference-time sparsification is a promising path to deploy large language models (LLMs) on resource-constrained devices, yet existing training-free methods typically estimate feed…

cs.CV2025

PixelMan: Consistent Object Editing with Diffusion Models via Pixel Manipulation and Generation

Liyao Jiang, Negar Hassanpour, Mohammad Salameh +4

Recent research explores the potential of Diffusion Models (DMs) for consistent object editing, which aims to modify object position, size, and composition, etc., while preserving…

cs.CV2024

FRAP: Faithful and Realistic Text-to-Image Generation with Adaptive Prompt Weighting

Liyao Jiang, Negar Hassanpour, Mohammad Salameh +4

Text-to-image (T2I) diffusion models have demonstrated impressive capabilities in generating high-quality images given a text prompt. However, ensuring the prompt-image alignment r…

cs.CV2024

FunEditor: Achieving Complex Image Edits via Function Aggregation with Diffusion Models

Mohammadreza Samadi, Fred X. Han, Mohammad Salameh +4

Diffusion models have demonstrated outstanding performance in generative tasks, making them ideal candidates for image editing. Recent studies highlight their ability to apply desi…

cs.CV2024

Building Optimal Neural Architectures using Interpretable Knowledge

Keith G. Mills, Fred X. Han, Mohammad Salameh +5

Neural Architecture Search is a costly practice. The fact that a search space can span a vast number of design choices with each architecture evaluation taking nontrivial overhead…

eess.IV2024★ 9 cited

CascadedGaze: Efficiency in Global Context Extraction for Image Restoration

Amirhosein Ghasemabadi, Muhammad Kamran Janjua, Mohammad Salameh +3

Image restoration tasks traditionally rely on convolutional neural networks. However, given the local nature of the convolutional operator, they struggle to capture global informat…