9 citations · 21 across the 12 of their papers we have counts for
12 papers
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…
Applying Graph Explanation to Operator Fusion
Keith G. Mills, Muhammad Fetrat Qharabagh, Weichen Qiu +5
Layer fusion techniques are critical to improving the inference efficiency of deep neural networks (DNN) for deployment. Fusion aims to lower inference costs by reducing data trans…
QuaSeDiMo: Quantifiable Quantization Sensitivity of Diffusion Models
Keith G. Mills, Mohammad Salameh, Ruichen Chen +3
Diffusion Models (DM) have democratized AI image generation through an iterative denoising process. Quantization is a major technique to alleviate the inference cost and reduce the…
Learning Truncated Causal History Model for Video Restoration
Amirhosein Ghasemabadi, Muhammad Kamran Janjua, Mohammad Salameh +1
One key challenge to video restoration is to model the transition dynamics of video frames governed by motion. In this work, we propose TURTLE to learn the truncated causal history…
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…
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…
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…
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…
Reparameterization through Spatial Gradient Scaling
Alexander Detkov, Mohammad Salameh, Muhammad Fetrat Qharabagh +4
Reparameterization aims to improve the generalization of deep neural networks by transforming convolutional layers into equivalent multi-branched structures during training. Howeve…
A General-Purpose Transferable Predictor for Neural Architecture Search
Fred X. Han, Keith G. Mills, Fabian Chudak +6
Understanding and modelling the performance of neural architectures is key to Neural Architecture Search (NAS). Performance predictors have seen widespread use in low-cost NAS and…
AIO-P: Expanding Neural Performance Predictors Beyond Image Classification
Keith G. Mills, Di Niu, Mohammad Salameh +6
Evaluating neural network performance is critical to deep neural network design but a costly procedure. Neural predictors provide an efficient solution by treating architectures as…
GENNAPE: Towards Generalized Neural Architecture Performance Estimators
Keith G. Mills, Fred X. Han, Jialin Zhang +6
Predicting neural architecture performance is a challenging task and is crucial to neural architecture design and search. Existing approaches either rely on neural performance pred…