22 citations · 77 across the 12 of their papers we have counts for
21 papers · 1 filter
SAM 3: Segment Anything with Concepts
Nicolas Carion, Laura Gustafson, Yuan-Ting Hu +35
We present Segment Anything Model (SAM) 3, a unified model that detects, segments, and tracks objects in images and videos based on concept prompts, which we define as either short…
Enrich and Detect: Video Temporal Grounding with Multimodal LLMs
Shraman Pramanick, Effrosyni Mavroudi, Yale Song +3
We introduce ED-VTG, a method for fine-grained video temporal grounding utilizing multi-modal large language models. Our approach harnesses the capabilities of multimodal LLMs to j…
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…
Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives
Kristen Grauman, Andrew Westbury, Lorenzo Torresani +98
We present Ego-Exo4D, a diverse, large-scale multimodal multiview video dataset and benchmark challenge. Ego-Exo4D centers around simultaneously-captured egocentric and exocentric…
Video-Mined Task Graphs for Keystep Recognition in Instructional Videos
Kumar Ashutosh, Santhosh Kumar Ramakrishnan, Triantafyllos Afouras +1
Procedural activity understanding requires perceiving human actions in terms of a broader task, where multiple keysteps are performed in sequence across a long video to reach a fin…
Learning to Ground Instructional Articles in Videos through Narrations
Effrosyni Mavroudi, Triantafyllos Afouras, Lorenzo Torresani
In this paper we present an approach for localizing steps of procedural activities in narrated how-to videos. To deal with the scarcity of labeled data at scale, we source the step…