495 citations · 580 across the 16 of their papers we have counts for
13 papers · 1 filter
Beyond Fixed Thresholds and Domain-Specific Benchmarks for Explainable Multi-Task Classification in Autonomous Vehicles
Maryam Sadat Hosseini Azad, Shahriar Baradaran Shokouhi
Scene understanding is a vital part of autonomous driving systems, which requires the use of deep learning models. Deep learning methods are intrinsically black box models, which l…
An End-to-End Decision-Aware Multi-Scale Attention-Based Model for Explainable Autonomous Driving
Maryam Sadat Hosseini Azad, Shahriar Baradaran Shokouhi, Amir Abbas Hamidi Imani +2
The application of computer vision is gradually increasing across various domains. They employ deep learning models with a black-box nature. Without the ability to explain the beha…
Enhancing Vehicle Make and Model Recognition with 3D Attention Modules
Narges Semiromizadeh, Omid Nejati Manzari, Shahriar B. Shokouhi +1
Vehicle make and model recognition (VMMR) is a crucial component of the Intelligent Transport System, garnering significant attention in recent years. VMMR has been widely utilized…
CSFNet: A Cosine Similarity Fusion Network for Real-Time RGB-X Semantic Segmentation of Driving Scenes
Danial Qashqai, Emad Mousavian, Shahriar Baradaran Shokouhi +1
Semantic segmentation, as a crucial component of complex visual interpretation, plays a fundamental role in autonomous vehicle vision systems. Recent studies have significantly imp…
Traffic Sign Recognition Using Local Vision Transformer
Ali Farzipour, Omid Nejati Manzari, Shahriar B. Shokouhi
Recognition of traffic signs is a crucial aspect of self-driving cars and driver assistance systems, and machine vision tasks such as traffic sign recognition have gained significa…
MedViT: A Robust Vision Transformer for Generalized Medical Image Classification
Omid Nejati Manzari, Hamid Ahmadabadi, Hossein Kashiani +2
Convolutional Neural Networks (CNNs) have advanced existing medical systems for automatic disease diagnosis. However, there are still concerns about the reliability of deep medical…
Robust Transformer with Locality Inductive Bias and Feature Normalization
Omid Nejati Manzari, Hossein Kashiani, Hojat Asgarian Dehkordi +1
Vision transformers have been demonstrated to yield state-of-the-art results on a variety of computer vision tasks using attention-based networks. However, research works in transf…
Pyramid Transformer for Traffic Sign Detection
Omid Nejati Manzari, Amin Boudesh, Shahriar B. Shokouhi
Traffic sign detection is a vital task in the visual system of self-driving cars and the automated driving system. Recently, novel Transformer-based models have achieved encouragin…
Multi-Expert Human Action Recognition with Hierarchical Super-Class Learning
Hojat Asgarian Dehkordi, Ali Soltani Nezhad, Hossein Kashiani +2
In still image human action recognition, existing studies have mainly leveraged extra bounding box information along with class labels to mitigate the lack of temporal information…
Online Visual Tracking with One-Shot Context-Aware Domain Adaptation
Hossein Kashiani, Amir Abbas Hamidi Imani, Shahriar Baradaran Shokouhi +1
Online learning policy makes visual trackers more robust against different distortions through learning domain-specific cues. However, the trackers adopting this policy fail to ful…
Ensembles of Deep Neural Networks for Action Recognition in Still Images
Sina Mohammadi, Sina Ghofrani Majelan, Shahriar B. Shokouhi
Despite the fact that notable improvements have been made recently in the field of feature extraction and classification, human action recognition is still challenging, especially…
Visual Object Tracking based on Adaptive Siamese and Motion Estimation Network
Hossein Kashiani, Shahriar B. Shokouhi
Recently, convolutional neural network (CNN) has attracted much attention in different areas of computer vision, due to its powerful abstract feature representation. Visual object…
Patchwise object tracking via structural local sparse appearance model
Hossein Kashiyani, Shahriar B. Shokouhi
In this paper, we propose a robust visual tracking method which exploits the relationships of targets in adjacent frames using patchwise joint sparse representation. Two sets of ov…