[CVPR2019 Paper Discussion] Kuniaki Saito @ Boston University - CrossMinds.ai
[CVPR2019 Paper Discussion] Kuniaki Saito @ Boston University
Aug 14, 20206 views
This episode features Kuniaki Saito, a PhD student at Boston University advised by Professor Kate Saenko. His paper "Strong-Weak Distribution Alignment for Adaptive Object Detection”, proposes an unsupervised adaptation for both label-rich and label-poor domains and reduce annotation costs associated with detection. 

This is a live recording of Saito presenting his paper during the CVPR poster session. He discussed his project in detail and how his proposed method is able to perform better than conventional domain adaptions. View full transcripts at Robin.ly: http://bit.ly/2HADme5
CVPR 2019