3 citations · 3 across the 5 of their papers we have counts for
7 papers · 1 filter
Locate Anything in Videos: Rethinking Efficient Generative Spatio-Temporal Video Grounding
Hanoona Rasheed, Haania Siddiqui, Ming-Hsuan Yang +2
Spatio-temporal video grounding (STVG) requires models to identify when a referred event occurs and localize the target entity throughout that interval. Existing multimodal large l…
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…
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…
Perception Encoder: The best visual embeddings are not at the output of the network
Daniel Bolya, Po-Yao Huang, Peize Sun +15
We introduce Perception Encoder (PE), a state-of-the-art vision encoder for image and video understanding trained via simple vision-language learning. Traditionally, vision encoder…
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…