activity
20172023
most citedFast Multi-frame Stereo Scene Flow with Motion Segmentation

29 citations · 80 across the 11 of their papers we have counts for

collaborators
Showing 2021Show all

6 papers · 1 filter

cs.CV2021★ 3 cited

Stacked Temporal Attention: Improving First-person Action Recognition by Emphasizing Discriminative Clips

Lijin Yang, Yifei Huang, Yusuke Sugano +1

First-person action recognition is a challenging task in video understanding. Because of strong ego-motion and a limited field of view, many backgrounds or noisy frames in a first-…

cs.CV2021★ 3 cited

Hand-Object Contact Prediction via Motion-Based Pseudo-Labeling and Guided Progressive Label Correction

Takuma Yagi, Md Tasnimul Hasan, Yoichi Sato

Every hand-object interaction begins with contact. Despite predicting the contact state between hands and objects is useful in understanding hand-object interactions, prior methods…

cs.CV2021

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

Kristen Grauman, Andrew Westbury, Eugene Byrne +82

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, outd…

cs.CV2021

Foreground-Aware Stylization and Consensus Pseudo-Labeling for Domain Adaptation of First-Person Hand Segmentation

Takehiko Ohkawa, Takuma Yagi, Atsushi Hashimoto +2

Hand segmentation is a crucial task in first-person vision. Since first-person images exhibit strong bias in appearance among different environments, adapting a pre-trained segment…

cs.CV2021★ 3 cited

EPIC-KITCHENS-100 Unsupervised Domain Adaptation Challenge for Action Recognition 2021: Team M3EM Technical Report

Lijin Yang, Yifei Huang, Yusuke Sugano +1

In this report, we describe the technical details of our submission to the 2021 EPIC-KITCHENS-100 Unsupervised Domain Adaptation Challenge for Action Recognition. Leveraging multip…

cs.HC2021★ 12 cited

GO-Finder: A Registration-Free Wearable System for Assisting Users in Finding Lost Objects via Hand-Held Object Discovery

Takuma Yagi, Takumi Nishiyasu, Kunimasa Kawasaki +2

People spend an enormous amount of time and effort looking for lost objects. To help remind people of the location of lost objects, various computational systems that provide infor…