1 citations · 1 across the 10 of their papers we have counts for
10 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…
FlameVQA: A Physically-Grounded UAV Wildfire VQA Benchmark with Radiometric Thermal Supervision
Mobin Habibpour, John Spodnik, Niloufar Alipour Talemi +1
Wildfire monitoring from UAVs requires reliable reasoning over complex aerial scenes, where smoke, scale variation, and occlusions often limit RGB-only interpretation. We introduce…
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…
WildFireVQA: A Large-Scale Radiometric Thermal VQA Benchmark for Aerial Wildfire Monitoring
Mobin Habibpour, Niloufar Alipour Talemi, John Spodnik +2
Wildfire monitoring requires timely, actionable situational awareness from airborne platforms, yet existing aerial visual question answering (VQA) benchmarks do not evaluate wildfi…
Agentic AI in Remote Sensing: Foundations, Taxonomy, and Emerging Systems
Niloufar Alipour Talemi, Julia Boone, Fatemeh Afghah
The paradigm of Earth Observation analysis is shifting from static deep learning models to autonomous agentic AI. Although recent vision foundation models and multimodal large lang…
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…
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…
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…
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…