74 citations · 152 across the 19 of their papers we have counts for
21 papers
Grounding Degradations in Natural Language for All-In-One Video Restoration
Muhammad Kamran Janjua, Amirhosein Ghasemabadi, Kunlin Zhang +3
In this work, we propose an all-in-one video restoration framework that grounds degradation-aware semantic context of video frames in natural language via foundation models, offeri…
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…
Incorporating Explanations into Human-Machine Interfaces for Trust and Situation Awareness in Autonomous Vehicles
Shahin Atakishiyev, Mohammad Salameh, Randy Goebel
Autonomous vehicles often make complex decisions via machine learning-based predictive models applied to collected sensor data. While this combination of methods provides a foundat…
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…
Safety Implications of Explainable Artificial Intelligence in End-to-End Autonomous Driving
Shahin Atakishiyev, Mohammad Salameh, Randy Goebel
The end-to-end learning pipeline is gradually creating a paradigm shift in the ongoing development of highly autonomous vehicles (AVs), largely due to advances in deep learning, th…
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…
Explaining Autonomous Driving Actions with Visual Question Answering
Shahin Atakishiyev, Mohammad Salameh, Housam Babiker +1
The end-to-end learning ability of self-driving vehicles has achieved significant milestones over the last decade owing to rapid advances in deep learning and computer vision algor…
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…
Explainable Artificial Intelligence for Autonomous Driving: A Comprehensive Overview and Field Guide for Future Research Directions
Shahin Atakishiyev, Mohammad Salameh, Hengshuai Yao +1
Autonomous driving has achieved significant milestones in research and development over the last two decades. There is increasing interest in the field as the deployment of autonom…
Towards Safe, Explainable, and Regulated Autonomous Driving
Shahin Atakishiyev, Mohammad Salameh, Hengshuai Yao +1
There has been recent and growing interest in the development and deployment of autonomous vehicles, encouraged by the empirical successes of powerful artificial intelligence techn…
LNAS: Learning to Optimize Neural Architectures via Continuous-Action Reinforcement Learning
Keith G. Mills, Fred X. Han, Mohammad Salameh +6
Neural architecture search (NAS) has achieved remarkable results in deep neural network design. Differentiable architecture search converts the search over discrete architectures i…
Generative Adversarial Neural Architecture Search
Seyed Saeed Changiz Rezaei, Fred X. Han, Di Niu +5
Despite the empirical success of neural architecture search (NAS) in deep learning applications, the optimality, reproducibility and cost of NAS schemes remain hard to assess. In t…
Neural Architecture Search For Keyword Spotting
Tong Mo, Yakun Yu, Mohammad Salameh +2
Deep neural networks have recently become a popular solution to keyword spotting systems, which enable the control of smart devices via voice. In this paper, we apply neural archit…