TQ-L.C2.2
INTERPRETABLE CONCEPT-BASED PROTOTYPICAL NETWORKS FOR FEW-SHOT LEARNING
Mohammad Reza Zarei, Majid Komeili, Carleton University, Canada
Session:
Weakly Supervised and Unsupervised Learning
Track:
Applications of Machine Learning
Location:
Room C2
Presentation Time:
Tue, 18 Oct, 20:15 - 20:30 China Standard Time (UTC +8)
Tue, 18 Oct, 14:15 - 14:30 Central European Time (UTC +1)
Tue, 18 Oct, 12:15 - 12:30 UTC
Tue, 18 Oct, 08:15 - 08:30 Eastern Time (UTC -5)
Tue, 18 Oct, 14:15 - 14:30 Central European Time (UTC +1)
Tue, 18 Oct, 12:15 - 12:30 UTC
Tue, 18 Oct, 08:15 - 08:30 Eastern Time (UTC -5)
Session Chair:
Truong Nguyen, UCSD
Presentation
Discussion
Resources
No resources available.
Session TQ-L.C2
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