2 citations · 4 across the 13 of their papers we have counts for
5 papers · 1 filter
PCRI: Measuring Context Robustness in Multimodal Models for Enterprise Applications
Hitesh Laxmichand Patel, Amit Agarwal, Srikant Panda +6
The reliability of Multimodal Large Language Models (MLLMs) in real-world settings is often undermined by sensitivity to irrelevant or distracting visual context, an aspect not cap…
RCI: A Score for Evaluating Global and Local Reasoning in Multimodal Benchmarks
Amit Agarwal, Hitesh Laxmichand Patel, Srikant Panda +7
Multimodal Large Language Models (MLLMs) have achieved impressive results on vision-language benchmarks, yet it remains unclear whether these benchmarks assess genuine global reaso…
FS-DAG: Few Shot Domain Adapting Graph Networks for Visually Rich Document Understanding
Amit Agarwal, Srikant Panda, Kulbhushan Pachauri
In this work, we propose Few Shot Domain Adapting Graph (FS-DAG), a scalable and efficient model architecture for visually rich document understanding (VRDU) in few-shot settings.…
Crowdsource, Crawl, or Generate? Creating SEA-VL, a Multicultural Vision-Language Dataset for Southeast Asia
Samuel Cahyawijaya, Holy Lovenia, Joel Ruben Antony Moniz +89
Southeast Asia (SEA) is a region of extraordinary linguistic and cultural diversity, yet it remains significantly underrepresented in vision-language (VL) research. This often resu…
MVTamperBench: Evaluating Robustness of Vision-Language Models
Amit Agarwal, Srikant Panda, Angeline Charles +8
Multimodal Large Language Models (MLLMs), are recent advancement of Vision-Language Models (VLMs) that have driven major advances in video understanding. However, their vulnerabili…