output
20022026
most citedDynamics of person-to-person interactions from distributed RFID sensor networks

850 citations

Showing cs.CVShow all

13 papers · 1 filter

cs.CV2021

How You Move Your Head Tells What You Do: Self-supervised Video Representation Learning with Egocentric Cameras and IMU Sensors

Satoshi Tsutsui, Ruta Desai, Karl Ridgeway

Understanding users' activities from head-mounted cameras is a fundamental task for Augmented and Virtual Reality (AR/VR) applications. A typical approach is to train a classifier…

cs.CV2020

Weakly-Supervised Cross-Domain Adaptation for Endoscopic Lesions Segmentation

Jiahua Dong, Yang Cong, Gan Sun +3

Weakly-supervised learning has attracted growing research attention on medical lesions segmentation due to significant saving in pixel-level annotation cost. However, 1) most exist…

cs.CV20201 cited

Whose hand is this? Person Identification from Egocentric Hand Gestures

Satoshi Tsutsui, Yanwei Fu, David Crandall

Recognizing people by faces and other biometrics has been extensively studied in computer vision. But these techniques do not work for identifying the wearer of an egocentric (firs…

cs.CV20201 cited

Adversarial Dual Distinct Classifiers for Unsupervised Domain Adaptation

Taotao Jing, Zhengming Ding

Unsupervised Domain adaptation (UDA) attempts to recognize the unlabeled target samples by building a learning model from a differently-distributed labeled source domain. Conventio…

cs.CV20205 cited

Discriminative Cross-Domain Feature Learning for Partial Domain Adaptation

Taotao Jing, Ming Shao, Zhengming Ding

Partial domain adaptation aims to adapt knowledge from a larger and more diverse source domain to a smaller target domain with less number of classes, which has attracted appealing…

cs.CV202011 cited

A Computational Model of Early Word Learning from the Infant's Point of View

Satoshi Tsutsui, Arjun Chandrasekaran, Md Alimoor Reza +2

Human infants have the remarkable ability to learn the associations between object names and visual objects from inherently ambiguous experiences. Researchers in cognitive science…