most citedAll Languages Matter: Evaluating LMMs on Culturally Diverse 100 Languages

1 citations · 1 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2025

How Good are Foundation Models in Step-by-Step Embodied Reasoning?

Dinura Dissanayake, Ahmed Heakl, Omkar Thawakar +9

Embodied agents operating in the physical world must make decisions that are not only effective but also safe, spatially coherent, and grounded in context. While recent advances in…

cs.CL2025

A Culturally-diverse Multilingual Multimodal Video Benchmark & Model

Bhuiyan Sanjid Shafique, Ashmal Vayani, Muhammad Maaz +26

Large multimodal models (LMMs) have recently gained attention due to their effectiveness to understand and generate descriptions of visual content. Most existing LMMs are in Englis…

cs.CV2025

DriveLMM-o1: A Step-by-Step Reasoning Dataset and Large Multimodal Model for Driving Scenario Understanding

Ayesha Ishaq, Jean Lahoud, Ketan More +10

While large multimodal models (LMMs) have demonstrated strong performance across various Visual Question Answering (VQA) tasks, certain challenges require complex multi-step reason…

cs.CV2025

LlamaV-o1: Rethinking Step-by-step Visual Reasoning in LLMs

Omkar Thawakar, Dinura Dissanayake, Ketan More +12

Reasoning is a fundamental capability for solving complex multi-step problems, particularly in visual contexts where sequential step-wise understanding is essential. Existing appro…

cs.CV2024★ 1 cited

All Languages Matter: Evaluating LMMs on Culturally Diverse 100 Languages

Ashmal Vayani, Dinura Dissanayake, Hasindri Watawana +66

Existing Large Multimodal Models (LMMs) generally focus on only a few regions and languages. As LMMs continue to improve, it is increasingly important to ensure they understand cul…