6 papers
Mobile-VideoGPT: Fast and Accurate Model for Mobile Video Understanding
Abdelrahman Shaker, Muhammad Maaz, Chenhui Gou +3
Video understanding models often struggle with high computational requirements, extensive parameter counts, and slow inference speed, making them inefficient for practical use. To…
Video-R2: Reinforcing Consistent and Grounded Reasoning in Multimodal Language Models
Muhammad Maaz, Hanoona Rasheed, Fahad Shahbaz Khan +1
Reasoning over dynamic visual content remains a central challenge for multimodal large language models. Recent thinking models generate explicit reasoning traces for interpretabili…
Video-CoM: Interactive Video Reasoning via Chain of Manipulations
Hanoona Rasheed, Mohammed Zumri, Muhammad Maaz +3
Recent multimodal large language models (MLLMs) have advanced video understanding, yet most still "think about videos" ie once a video is encoded, reasoning unfolds entirely in tex…
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…
PerceptionLM: Open-Access Data and Models for Detailed Visual Understanding
Jang Hyun Cho, Andrea Madotto, Effrosyni Mavroudi +26
Vision-language models are integral to computer vision research, yet many high-performing models remain closed-source, obscuring their data, design and training recipe. The researc…
VideoMathQA: Benchmarking Mathematical Reasoning via Multimodal Understanding in Videos
Hanoona Rasheed, Abdelrahman Shaker, Anqi Tang +4
Mathematical reasoning in real-world video settings presents a fundamentally different challenge than in static images or text. It requires interpreting fine-grained visual informa…