activity
20232025
most citedAnyMAL: An Efficient and Scalable Any-Modality Augmented Language Model

7 citations · 8 across the 3 of their papers we have counts for

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8 papers · 1 filter

cs.CV2025

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…

cs.CV2025

VITED: Video Temporal Evidence Distillation

Yujie Lu, Yale Song, William Wang +2

We investigate complex video question answering via chain-of-evidence reasoning -- identifying sequences of temporal spans from multiple relevant parts of the video, together with…

cs.CV2025

BIMBA: Selective-Scan Compression for Long-Range Video Question Answering

Md Mohaiminul Islam, Tushar Nagarajan, Huiyu Wang +2

Video Question Answering (VQA) in long videos poses the key challenge of extracting relevant information and modeling long-range dependencies from many redundant frames. The self-a…

cs.CV2024

Propose, Assess, Search: Harnessing LLMs for Goal-Oriented Planning in Instructional Videos

Md Mohaiminul Islam, Tushar Nagarajan, Huiyu Wang +4

Goal-oriented planning, or anticipating a series of actions that transition an agent from its current state to a predefined objective, is crucial for developing intelligent assista…

cs.CV20241 cited

Unlocking Exocentric Video-Language Data for Egocentric Video Representation Learning

Zi-Yi Dou, Xitong Yang, Tushar Nagarajan +5

We present EMBED (Egocentric Models Built with Exocentric Data), a method designed to transform exocentric video-language data for egocentric video representation learning. Large-s…

cs.CV2024

ExpertAF: Expert Actionable Feedback from Video

Kumar Ashutosh, Tushar Nagarajan, Georgios Pavlakos +2

Feedback is essential for learning a new skill or improving one's current skill-level. However, current methods for skill-assessment from video only provide scores or compare demon…