31 citations · 186 across the 67 of their papers we have counts for
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Trained Models Tell Us How to Make Them Robust to Spurious Correlation without Group Annotation
Mahdi Ghaznavi, Hesam Asadollahzadeh, Fahimeh Hosseini Noohdani +5
Classifiers trained with Empirical Risk Minimization (ERM) tend to rely on attributes that have high spurious correlation with the target. This can degrade the performance on under…
Diffusion Beats Autoregressive: An Evaluation of Compositional Generation in Text-to-Image Models
Arash Marioriyad, Parham Rezaei, Mahdieh Soleymani Baghshah +1
Text-to-image (T2I) generative models, such as Stable Diffusion and DALL-E, have shown remarkable proficiency in producing high-quality, realistic, and natural images from textual…
Attention Overlap Is Responsible for The Entity Missing Problem in Text-to-image Diffusion Models!
Arash Marioriyad, Mohammadali Banayeeanzade, Reza Abbasi +2
Text-to-image diffusion models, such as Stable Diffusion and DALL-E, are capable of generating high-quality, diverse, and realistic images from textual prompts. However, they somet…
IBO: Inpainting-Based Occlusion to Enhance Explainable Artificial Intelligence Evaluation in Histopathology
Pardis Afshar, Sajjad Hashembeiki, Pouya Khani +2
Histopathological image analysis is crucial for accurate cancer diagnosis and treatment planning. While deep learning models, especially convolutional neural networks, have advance…
Universal Novelty Detection Through Adaptive Contrastive Learning
Hossein Mirzaei, Mojtaba Nafez, Mohammad Jafari +5
Novelty detection is a critical task for deploying machine learning models in the open world. A crucial property of novelty detection methods is universality, which can be interpre…
Snuffy: Efficient Whole Slide Image Classifier
Hossein Jafarinia, Alireza Alipanah, Danial Hamdi +3
Whole Slide Image (WSI) classification with multiple instance learning (MIL) in digital pathology faces significant computational challenges. Current methods mostly rely on extensi…
Deciphering the Role of Representation Disentanglement: Investigating Compositional Generalization in CLIP Models
Reza Abbasi, Mohammad Hossein Rohban, Mahdieh Soleymani Baghshah
CLIP models have recently shown to exhibit Out of Distribution (OoD) generalization capabilities. However, Compositional Out of Distribution (C-OoD) generalization, which is a cruc…
GABInsight: Exploring Gender-Activity Binding Bias in Vision-Language Models
Ali Abdollahi, Mahdi Ghaznavi, Mohammad Reza Karimi Nejad +6
Vision-language models (VLMs) are intensively used in many downstream tasks, including those requiring assessments of individuals appearing in the images. While VLMs perform well i…
Khayyam Challenge (PersianMMLU): Is Your LLM Truly Wise to The Persian Language?
Omid Ghahroodi, Marzia Nouri, Mohammad Vali Sanian +5
Evaluating Large Language Models (LLMs) is challenging due to their generative nature, necessitating precise evaluation methodologies. Additionally, non-English LLM evaluation lags…
Language Plays a Pivotal Role in the Object-Attribute Compositional Generalization of CLIP
Reza Abbasi, Mohammad Samiei, Mohammad Hossein Rohban +1
Vision-language models, such as CLIP, have shown promising Out-of-Distribution (OoD) generalization under various types of distribution shifts. Recent studies attempted to investig…
CRISPR: Ensemble Model
Mohammad Rostami, Amin Ghariyazi, Hamed Dashti +2
Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) is a gene editing technology that has revolutionized the fields of biology and medicine. However, one of the chal…