495 citations · 511 across the 18 of their papers we have counts for
18 papers
SAMPLe: SAM-based Optimizer for Prompt Learning in VLMs
Hossein Rajoli, Fatemeh Lotfi, Niloufar Alipour Talemi +3
Pre-trained Vision-Language Models (VLMs) like CLIP have proven highly effective as foundation models for various downstream applications. However, prompt learning in VLMs encounte…
Hyper-ICL: Attention Calibration with Hyperbolic Anchor Distillation for Multimodal In-Context Learning
Niloufar Alipour Talemi, Hossein Kashiani, Fatemeh Afghah
Multimodal In-Context Learning (ICL) has emerged as a practical inference paradigm for Multimodal Large Language Models, where a small set of interleaved image-text In-Context Demo…
PromptMAD: Cross-Modal Prompting for Multi-Class Visual Anomaly Localization
Duncan McCain, Hossein Kashiani, Fatemeh Afghah
Visual anomaly detection in multi-class settings poses significant challenges due to the diversity of object categories, the scarcity of anomalous examples, and the presence of cam…
FreqDebias: Towards Generalizable Deepfake Detection via Consistency-Driven Frequency Debiasing
Hossein Kashiani, Niloufar Alipour Talemi, Fatemeh Afghah
Deepfake detectors often struggle to generalize to novel forgery types due to biases learned from limited training data. In this paper, we identify a new type of model bias in the…
DiSa: Directional Saliency-Aware Prompt Learning for Generalizable Vision-Language Models
Niloufar Alipour Talemi, Hossein Kashiani, Hossein R. Nowdeh +1
Prompt learning has emerged as a powerful paradigm for adapting vision-language models such as CLIP to downstream tasks. However, existing methods often overfit to seen data, leadi…
ROADS: Robust Prompt-driven Multi-Class Anomaly Detection under Domain Shift
Hossein Kashiani, Niloufar Alipour Talemi, Fatemeh Afghah
Recent advancements in anomaly detection have shifted focus towards Multi-class Unified Anomaly Detection (MUAD), offering more scalable and practical alternatives compared to trad…
Style-Pro: Style-Guided Prompt Learning for Generalizable Vision-Language Models
Niloufar Alipour Talemi, Hossein Kashiani, Fatemeh Afghah
Pre-trained Vision-language (VL) models, such as CLIP, have shown significant generalization ability to downstream tasks, even with minimal fine-tuning. While prompt learning has e…
CATFace: Cross-Attribute-Guided Transformer with Self-Attention Distillation for Low-Quality Face Recognition
Niloufar Alipour Talemi, Hossein Kashiani, Nasser M. Nasrabadi
Although face recognition (FR) has achieved great success in recent years, it is still challenging to accurately recognize faces in low-quality images due to the obscured facial de…
Multi-Context Dual Hyper-Prior Neural Image Compression
Atefeh Khoshkhahtinat, Ali Zafari, Piyush M. Mehta +3
Transform and entropy models are the two core components in deep image compression neural networks. Most existing learning-based image compression methods utilize convolutional-bas…
Towards Generalizable Morph Attack Detection with Consistency Regularization
Hossein Kashiani, Niloufar Alipour Talemi, Mohammad Saeed Ebrahimi Saadabadi +1
Though recent studies have made significant progress in morph attack detection by virtue of deep neural networks, they often fail to generalize well to unseen morph attacks. With n…
CCFace: Classification Consistency for Low-Resolution Face Recognition
Mohammad Saeed Ebrahimi Saadabadi, Sahar Rahimi Malakshan, Hossein Kashiani +1
In recent years, deep face recognition methods have demonstrated impressive results on in-the-wild datasets. However, these methods have shown a significant decline in performance…
AAFACE: Attribute-aware Attentional Network for Face Recognition
Niloufar Alipour Talemi, Hossein Kashiani, Sahar Rahimi Malakshan +4
In this paper, we present a new multi-branch neural network that simultaneously performs soft biometric (SB) prediction as an auxiliary modality and face recognition (FR) as the ma…
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…
Robust Ensemble Morph Detection with Domain Generalization
Hossein Kashiani, Shoaib Meraj Sami, Sobhan Soleymani +1
Although a substantial amount of studies is dedicated to morph detection, most of them fail to generalize for morph faces outside of their training paradigm. Moreover, recent morph…
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…
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…