TA-L.C2: Self-Supervised Learning
Tue, 18 Oct, 16:30 - 18:30 China Standard Time (UTC +8)
Tue, 18 Oct, 10:30 - 12:30 Central European Time (UTC +1)
Tue, 18 Oct, 08:30 - 10:30 UTC
Tue, 18 Oct, 04:30 - 06:30 Eastern Time (UTC -5)
Lecture
Location: Room C2
Session Chair: Onur Guleryuz, Google
Track: Applications of Machine Learning

TA-L.C2.1: Refining Self-Supervised Learning in Imaging: Beyond Linear Metric
Bo Jiang, Hamid Krim, Tianfu Wu, North Carolina State University, United States of America; Derya Cansever, US Army Research Office, United States of America

TA-L.C2.2: SCENE REPRESENTATION LEARNING FROM VIDEOS USING SELF-SUPERVISED AND WEAKLY-SUPERVISED TECHNIQUES
Raghuveer Peri, University of Southern California, United States of America; Srinivas Parthasarathy, Shiva Sundaram, Amazon Inc., United States of America

TA-L.C2.3: SELF-SUPERVISED PRETRAINING FOR DEEP HASH-BASED IMAGE RETRIEVAL
Haeyoon Yang, Young Kyun Jang, Isaac Kang, Nam Ik Cho, Seoul National University, Korea, Republic of

TA-L.C2.4: Self-SuperFlow: Self-supervised Scene Flow Prediction in Stereo Sequences
Katharina Bendig, Technische Universität Kaiserslautern, Germany; René Schuster, Didier Stricker, German Research Center for Artificial Intelligence - DFKI, Germany

TA-L.C2.5: Object-Aware Self-supervised Multi-Label Learning
Kaixin Xu, Ziyuan Zhao, Zeng Zeng, Institute of Infocomm Research, Singapore; Liyang Liu, Bharadwaj Veeravalli, National University of Singapore, Singapore