6 papers
HueManity: Probing Fine-Grained Visual Perception in MLLMs
Rynaa Grover, Jayant Sravan Tamarapalli, Sahiti Yerramilli +1
Recent Multimodal Large Language Models (MLLMs) demonstrate strong high-level visual reasoning on tasks such as visual question answering and image captioning. Yet existing benchma…
Semantic Augmentation in Images using Language
Sahiti Yerramilli, Jayant Sravan Tamarapalli, Tanmay Girish Kulkarni +2
Deep Learning models are incredibly data-hungry and require very large labeled datasets for supervised learning. As a consequence, these models often suffer from overfitting, limit…
Attribution Regularization for Multimodal Paradigms
Sahiti Yerramilli, Jayant Sravan Tamarapalli, Jonathan Francis +1
Multimodal machine learning has gained significant attention in recent years due to its potential for integrating information from multiple modalities to enhance learning and decis…
MaRVL-QA: A Benchmark for Mathematical Reasoning over Visual Landscapes
Nilay Pande, Sahiti Yerramilli, Jayant Sravan Tamarapalli +1
A key frontier for Multimodal Large Language Models (MLLMs) is the ability to perform deep mathematical and spatial reasoning directly from images, moving beyond their established…
CountQA: How Well Do MLLMs Count in the Wild?
Jayant Sravan Tamarapalli, Rynaa Grover, Nilay Pande +1
Multimodal Large Language Models (MLLMs) demonstrate remarkable fluency in understanding visual scenes, yet they exhibit a critical lack in a fundamental cognitive skill: object co…
GeoChain: Multimodal Chain-of-Thought for Geographic Reasoning
Sahiti Yerramilli, Nilay Pande, Rynaa Grover +1
This paper introduces GeoChain, a large-scale benchmark for evaluating step-by-step geographic reasoning in multimodal large language models (MLLMs). Leveraging 1.46 million Mapill…