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Miguel Martin

4 accepted papers

2025

PerceptionLM: Open-Access Data and Models for Detailed Visual Understanding

Jang Hyun Cho, Andrea Madotto, Effrosyni Mavroudi, Triantafyllos Afouras, Tushar Nagarajan, Muhammad Maaz +23
NeurIPS 2025spotlight

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 research community has responded by using distillation from black-box models to label training data, achieving strong benchmark…

Cited by 0Source ↗code ↗Scholar ↗
2024

Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives

Kristen Grauman, Andrew Westbury, Lorenzo Torresani, Kris Kitani, Jitendra Malik, Triantafyllos Afouras +94
CVPR 2024poster

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 of skilled human activities (e.g. sports music dance bike repair). 740 participants from 13 cities worldwide perform…

Cited by 197Source ↗PDF ↗code ↗Scholar ↗
2023

Ego4D Goal-Step: Toward Hierarchical Understanding of Procedural Activities

Yale Song, Gene Byrne, Tushar Nagarajan, Huiyu Wang, Miguel Martin, Lorenzo Torresani
NeurIPS 2023spotlight

Human activities are goal-oriented and hierarchical, comprising primary goals at the top level, sequences of steps and substeps in the middle, and atomic actions at the lowest level. Recognizing human activities thus requires relating atomic actions and steps to their functional objectives (what the…

Cited by 35Source ↗PDF ↗Scholar ↗
2022

Ego4D: Around the World in 3,000 Hours of Egocentric Video

Kristen Grauman, Andrew Westbury, Eugene Byrne, Zachary Chavis, Antonino Furnari, Rohit Girdhar +79
CVPR 2022oral

We introduce Ego4D, a massive-scale egocentric video dataset and benchmark suite. It offers 3,670 hours of daily-life activity video spanning hundreds of scenarios (household, outdoor, workplace, leisure, etc.) captured by 931 unique camera wearers from 74 worldwide locations and 9 different countri…

Cited by 1162PDF ↗code ↗Scholar ↗