1 citations · 2 across the 7 of their papers we have counts for
7 papers
A Benchmark for Omni-Modal Reasoning in Long Videos
Mohammed Irfan Kurpath, Jaseel Muhammad Kaithakkodan, Jinxing Zhou +12
Long-form omni-modal video understanding requires integrating vision, speech, and ambient audio with coherent long-context reasoning. Existing video benchmarks often trade off temp…
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…
Agent-X: Evaluating Deep Multimodal Reasoning in Vision-Centric Agentic Tasks
Tajamul Ashraf, Amal Saqib, Hanan Ghani +11
Deep reasoning is fundamental for solving complex tasks, especially in vision-centric scenarios that demand sequential, multimodal understanding. However, existing benchmarks typic…
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…
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…
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…
CDChat: A Large Multimodal Model for Remote Sensing Change Description
Mubashir Noman, Noor Ahsan, Muzammal Naseer +4
Large multimodal models (LMMs) have shown encouraging performance in the natural image domain using visual instruction tuning. However, these LMMs struggle to describe the content…