48 citations · 127 across the 19 of their papers we have counts for
22 papers
OpenVAM: Open-World Visual Attention Modeling with VLMs
Kiana Hooshanfar, Amirhossein Kazerouni, Alireza Hosseini +2
Predicting human gaze is a core capability for applications ranging from web/UI design analysis to robotics and human-computer interaction. Yet, most visual attention modeling meth…
AVIS: Adaptive Test-Time Scaling for Vision-Language Models
Ahmadreza Jeddi, Minh Ngoc Le, Amirhossein Kazerouni +8
Modern Vision-Language Models (VLMs) benefit from chain-of-thought prompting and test-time scaling, but these gains often come with prohibitive inference cost due to large visual c…
BurstGP: Enhancing Raw Burst Image Super Resolution with Generative Priors
Dong Huo, Tristan Aumentado-Armstrong, Samrudhdhi B. Rangrej +8
Burst image super resolution (BISR) aims to construct a single high-resolution (HR) image by aggregating information from multiple low-resolution (LR) frames, relying on temporal r…
Face2Scene: Using Facial Degradation as an Oracle for Diffusion-Based Scene Restoration
Amirhossein Kazerouni, Maitreya Suin, Tristan Aumentado-Armstrong +6
Recent advances in image restoration have enabled high-fidelity recovery of faces from degraded inputs using reference-based face restoration models (Ref-FR). However, such methods…
LIFT: Latent Implicit Functions for Task- and Data-Agnostic Encoding
Amirhossein Kazerouni, Soroush Mehraban, Michael Brudno +1
Implicit Neural Representations (INRs) are proving to be a powerful paradigm in unifying task modeling across diverse data domains, offering key advantages such as memory efficienc…
A Unified Theory of Sinusoidal Activation Families for Implicit Neural Representations
Alireza Morsali, MohammadJavad Vaez, Mohammadhossein Soltani +3
Implicit Neural Representations (INRs) model continuous signals with compact neural networks and have become a standard tool in vision, graphics, and signal processing. A central c…
SUM: Saliency Unification through Mamba for Visual Attention Modeling
Alireza Hosseini, Amirhossein Kazerouni, Saeed Akhavan +2
Visual attention modeling, important for interpreting and prioritizing visual stimuli, plays a significant role in applications such as marketing, multimedia, and robotics. Traditi…
Enhancing Efficiency in Vision Transformer Networks: Design Techniques and Insights
Moein Heidari, Reza Azad, Sina Ghorbani Kolahi +8
Intrigued by the inherent ability of the human visual system to identify salient regions in complex scenes, attention mechanisms have been seamlessly integrated into various Comput…
FuseNet: Self-Supervised Dual-Path Network for Medical Image Segmentation
Amirhossein Kazerouni, Sanaz Karimijafarbigloo, Reza Azad +3
Semantic segmentation, a crucial task in computer vision, often relies on labor-intensive and costly annotated datasets for training. In response to this challenge, we introduce Fu…
INCODE: Implicit Neural Conditioning with Prior Knowledge Embeddings
Amirhossein Kazerouni, Reza Azad, Alireza Hosseini +2
Implicit Neural Representations (INRs) have revolutionized signal representation by leveraging neural networks to provide continuous and smooth representations of complex data. How…
Foundational Models in Medical Imaging: A Comprehensive Survey and Future Vision
Bobby Azad, Reza Azad, Sania Eskandari +4
Foundation models, large-scale, pre-trained deep-learning models adapted to a wide range of downstream tasks have gained significant interest lately in various deep-learning proble…
Unlocking Fine-Grained Details with Wavelet-based High-Frequency Enhancement in Transformers
Reza Azad, Amirhossein Kazerouni, Alaa Sulaiman +4
Medical image segmentation is a critical task that plays a vital role in diagnosis, treatment planning, and disease monitoring. Accurate segmentation of anatomical structures and a…
Self-supervised Semantic Segmentation: Consistency over Transformation
Sanaz Karimijafarbigloo, Reza Azad, Amirhossein Kazerouni +3
Accurate medical image segmentation is of utmost importance for enabling automated clinical decision procedures. However, prevailing supervised deep learning approaches for medical…
Beyond Self-Attention: Deformable Large Kernel Attention for Medical Image Segmentation
Reza Azad, Leon Niggemeier, Michael Huttemann +5
Medical image segmentation has seen significant improvements with transformer models, which excel in grasping far-reaching contexts and global contextual information. However, the…
Laplacian-Former: Overcoming the Limitations of Vision Transformers in Local Texture Detection
Reza Azad, Amirhossein Kazerouni, Babak Azad +4
Vision Transformer (ViT) models have demonstrated a breakthrough in a wide range of computer vision tasks. However, compared to the Convolutional Neural Network (CNN) models, it ha…
DermoSegDiff: A Boundary-aware Segmentation Diffusion Model for Skin Lesion Delineation
Afshin Bozorgpour, Yousef Sadegheih, Amirhossein Kazerouni +2
Skin lesion segmentation plays a critical role in the early detection and accurate diagnosis of dermatological conditions. Denoising Diffusion Probabilistic Models (DDPMs) have rec…
Implicit Neural Representation in Medical Imaging: A Comparative Survey
Amirali Molaei, Amirhossein Aminimehr, Armin Tavakoli +4
Implicit neural representations (INRs) have gained prominence as a powerful paradigm in scene reconstruction and computer graphics, demonstrating remarkable results. By utilizing n…
An intelligent modular real-time vision-based system for environment perception
Amirhossein Kazerouni, Amirhossein Heydarian, Milad Soltany +3
A significant portion of driving hazards is caused by human error and disregard for local driving regulations; Consequently, an intelligent assistance system can be beneficial. Thi…
Advances in Medical Image Analysis with Vision Transformers: A Comprehensive Review
Reza Azad, Amirhossein Kazerouni, Moein Heidari +6
The remarkable performance of the Transformer architecture in natural language processing has recently also triggered broad interest in Computer Vision. Among other merits, Transfo…
Diffusion Models for Medical Image Analysis: A Comprehensive Survey
Amirhossein Kazerouni, Ehsan Khodapanah Aghdam, Moein Heidari +4
Denoising diffusion models, a class of generative models, have garnered immense interest lately in various deep-learning problems. A diffusion probabilistic model defines a forward…
HiFormer: Hierarchical Multi-scale Representations Using Transformers for Medical Image Segmentation
Moein Heidari, Amirhossein Kazerouni, Milad Soltany +4
Convolutional neural networks (CNNs) have been the consensus for medical image segmentation tasks. However, they suffer from the limitation in modeling long-range dependencies and…
Ensemble Neural Representation Networks
Milad Soltany Kadarvish, Hesam Mojtahedi, Hossein Entezari Zarch +4
Implicit Neural Representation (INR) has recently attracted considerable attention for storing various types of signals in continuous forms. The existing INR networks require lengt…