1 citations · 3 across the 11 of their papers we have counts for
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Limits and Gains of Test-Time Scaling in Vision-Language Reasoning
Mohammadjavad Ahmadpour, Amirmahdi Meighani, Payam Taebi +3
Test-time scaling (TTS) has emerged as a powerful paradigm for improving the reasoning ability of Large Language Models (LLMs) by allocating additional computation at inference, ye…
Understanding Counting Mechanisms in Large Language and Vision-Language Models
Hosein Hasani, Amirmohammad Izadi, Fatemeh Askari +4
Counting is one of the fundamental abilities of large language models (LLMs) and large vision-language models (LVLMs). This paper examines how these foundation models represent and…
Uncovering Grounding IDs: How External Cues Shape Multi-Modal Binding
Hosein Hasani, Amirmohammad Izadi, Fatemeh Askari +4
Large vision-language models (LVLMs) perform well on multimodal tasks, but their ability to reason and precisely align visual and textual information still has room for improvement…
Visual Structures Helps Visual Reasoning: Addressing the Binding Problem in VLMs
Amirmohammad Izadi, Mohammad Ali Banayeeanzade, Fatemeh Askari +4
Despite progress in Large Vision-Language Models (LVLMs), their capacity for visual reasoning is often limited by the binding problem: the failure to reliably associate perceptual…
T2I-FineEval: Fine-Grained Compositional Metric for Text-to-Image Evaluation
Seyed Mohammad Hadi Hosseini, Amir Mohammad Izadi, Ali Abdollahi +2
Although recent text-to-image generative models have achieved impressive performance, they still often struggle with capturing the compositional complexities of prompts including a…
Fine-Grained Alignment and Noise Refinement for Compositional Text-to-Image Generation
Amir Mohammad Izadi, Seyed Mohammad Hadi Hosseini, Soroush Vafaie Tabar +3
Text-to-image generative models have made significant advancements in recent years; however, accurately capturing intricate details in textual prompts-such as entity missing, attri…