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

7 citations · 11 across the 8 of their papers we have counts for

collaborators

9 papers

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.CV2024★ 1 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★ 2 cited

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…

cs.CV2024

Step Differences in Instructional Video

Tushar Nagarajan, Lorenzo Torresani

Comparing a user video to a reference how-to video is a key requirement for AR/VR technology delivering personalized assistance tailored to the user's progress. However, current ap…

cs.CV2024★ 1 cited

Video ReCap: Recursive Captioning of Hour-Long Videos

Md Mohaiminul Islam, Ngan Ho, Xitong Yang +3

Most video captioning models are designed to process short video clips of few seconds and output text describing low-level visual concepts (e.g., objects, scenes, atomic actions).…

cs.LG2023★ 7 cited

AnyMAL: An Efficient and Scalable Any-Modality Augmented Language Model

Seungwhan Moon, Andrea Madotto, Zhaojiang Lin +10

We present Any-Modality Augmented Language Model (AnyMAL), a unified model that reasons over diverse input modality signals (i.e. text, image, video, audio, IMU motion sensor), and…