TQ-L.C2: Weakly Supervised and Unsupervised Learning
Tue, 18 Oct, 20:00 - 22:00 China Standard Time (UTC +8)
Tue, 18 Oct, 14:00 - 16:00 Central European Time (UTC +1)
Tue, 18 Oct, 12:00 - 14:00 UTC
Tue, 18 Oct, 08:00 - 10:00 Eastern Time (UTC -5)
Lecture
Location: Room C2
Session Chair: Truong Nguyen, UCSD
Track: Applications of Machine Learning

TQ-L.C2.1: EPISODE DIFFICULTY BASED SAMPLING METHOD FOR FEW-SHOT CLASSIFICATION
Hochang Rhee, Nam Ik Cho, Seoul National University, Korea, Republic of

TQ-L.C2.2: INTERPRETABLE CONCEPT-BASED PROTOTYPICAL NETWORKS FOR FEW-SHOT LEARNING
Mohammad Reza Zarei, Majid Komeili, Carleton University, Canada

TQ-L.C2.3: CONTRASTIVE LEARNING FOR ONLINE SEMI-SUPERVISED GENERAL CONTINUAL LEARNING
Nicolas Michel, Romain Negrel, Giovanni Chierchia, Jean-François Bercher, ESIEE, France

TQ-L.C2.4: DEPTH IS ALL YOU NEED: SINGLE-STAGE WEAKLY SUPERVISED SEMANTIC SEGMENTATION FROM IMAGE-LEVEL SUPERVISION
Mustafa Ergul, Aydin Alatan, Middle East Technical University, Turkey

TQ-L.C2.5: RETHINKING UNSUPERVISED NEURAL SUPERPIXEL SEGMENTATION
Moshe Eliasof, Nir Ben Zikri, Eran Treister, Ben-Gurion University of the Negev, Israel

TQ-L.C2.6: UNSUPERVISED DOMAIN-ADAPTIVE PERSON RE-IDENTIFICATION WITH MULTI-CAMERA CONSTRAINTS
Shun Takeuchi, Sho Iwasaki, Genta Suzuki, Fujitsu Limited, Japan; Fei Li, Jiaqi Ning, Fujitsu Research and Development Center Co., Ltd., China