activity
20222026
most citedYour Out-of-Distribution Detection Method is Not Robust!

2 citations · 2 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CV2026

HaloProbe: Bayesian Detection and Mitigation of Object Hallucinations in Vision-Language Models

Reihaneh Zohrabi, Hosein Hasani, Akshita Gupta +3

Large vision-language models can produce object hallucinations in image descriptions, highlighting the need for effective detection and mitigation strategies. Prior work commonly r…

cs.AI2025

MEENA (PersianMMMU): Multimodal-Multilingual Educational Exams for N-level Assessment

Omid Ghahroodi, Arshia Hemmat, Marzia Nouri +8

Recent advancements in large vision-language models (VLMs) have primarily focused on English, with limited attention given to other languages. To address this gap, we introduce MEE…

cs.CV2025

Spurious-Aware Prototype Refinement for Reliable Out-of-Distribution Detection

Reihaneh Zohrabi, Hosein Hasani, Mahdieh Soleymani Baghshah +3

Out-of-distribution (OOD) detection is crucial for ensuring the reliability and safety of machine learning models in real-world applications, where they frequently face data distri…

cs.CV2023

Spuriosity Rankings for Free: A Simple Framework for Last Layer Retraining Based on Object Detection

Mohammad Azizmalayeri, Reza Abbasi, Amir Hosein Haji Mohammad rezaie +4

Deep neural networks have exhibited remarkable performance in various domains. However, the reliance of these models on spurious features has raised concerns about their reliabilit…

cs.CV2022★ 2 cited

Your Out-of-Distribution Detection Method is Not Robust!

Mohammad Azizmalayeri, Arshia Soltani Moakhar, Arman Zarei +3

Out-of-distribution (OOD) detection has recently gained substantial attention due to the importance of identifying out-of-domain samples in reliability and safety. Although OOD det…