495 citations · 564 across the 13 of their papers we have counts for
13 papers · 1 filter
Recursive Vision Language Models for General Symbolic Reasoning
Omid Nejati Manzari, Guillaume Lajoie, Hassan Rivaz
Hard symbolic-reasoning tasks such as Sudoku, maze pathfinding, and ARC remain challenging for LLMs due to their fixed-depth autoregressive reasoning, which limits systematic searc…
Sparse Spectral LoRA: Routed Experts for Medical VLMs
Omid Nejati Manzari, Hojat Asgariandehkordi, Taha Koleilat +2
Large vision-language models (VLMs) excel on general benchmarks but often lack robustness in medical imaging, where heterogeneous supervision induces cross-dataset interference and…
MedCLIPSeg: Probabilistic Vision-Language Adaptation for Data-Efficient and Generalizable Medical Image Segmentation
Taha Koleilat, Hojat Asgariandehkordi, Omid Nejati Manzari +3
Medical image segmentation remains challenging due to limited annotations for training, ambiguous anatomical features, and domain shifts. While vision-language models such as CLIP…
Med-VCD: Mitigating Hallucination for Medical Large Vision Language Models through Visual Contrastive Decoding
Zahra Mahdavi, Zahra Khodakaramimaghsoud, Hooman Khaloo +4
Large vision-language models (LVLMs) are now central to healthcare applications such as medical visual question answering and imaging report generation. Yet, these models remain vu…
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…
Medical Image Classification with KAN-Integrated Transformers and Dilated Neighborhood Attention
Omid Nejati Manzari, Hojat Asgariandehkordi, Taha Koleilat +2
Convolutional networks, transformers, hybrid models, and Mamba-based architectures have demonstrated strong performance across various medical image classification tasks. However,…
BEFUnet: A Hybrid CNN-Transformer Architecture for Precise Medical Image Segmentation
Omid Nejati Manzari, Javad Mirzapour Kaleybar, Hooman Saadat +1
The accurate segmentation of medical images is critical for various healthcare applications. Convolutional neural networks (CNNs), especially Fully Convolutional Networks (FCNs) li…
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…
Distilling Knowledge from CNN-Transformer Models for Enhanced Human Action Recognition
Hamid Ahmadabadi, Omid Nejati Manzari, Ahmad Ayatollahi
This paper presents a study on improving human action recognition through the utilization of knowledge distillation, and the combination of CNN and ViT models. The research aims to…
Dilated-UNet: A Fast and Accurate Medical Image Segmentation Approach using a Dilated Transformer and U-Net Architecture
Davoud Saadati, Omid Nejati Manzari, Sattar Mirzakuchaki
Medical image segmentation is crucial for the development of computer-aided diagnostic and therapeutic systems, but still faces numerous difficulties. In recent years, the commonly…
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…