MA2.L3.5
UNSUPERVISED DOMAIN ADAPTIVE SEMANTIC SEGMENTATION BASED ON CLIP-GUIDED PROTOTYPICAL CONTRASTIVE LEARNING
Kebin Liu, Chuang Zhu, Beijing University of Posts and Telecommunications, China
Session:
MA2.L3: Contrastive Learning for Image & Video Data Lecture
Track:
Visual Artificial Intelligence
Location:
Lecture Room 3
Presentation Time:
Mon, 28 Oct, 11:42 - 12:00 Gulf Standard Time (UTC +4)
Session Chair:
Hak Gu Kim, Chung-Ang University
Session MA2.L3
MA2.L3.1: Masked Momentum Contrastive Learning for Semantic Understanding by Observation
Jiantao Wu, University of Surrey, United Kingdom of Great Britain and Northern Ireland; Shentong Mo, Carnegie Mellon University, United States of America; Sara Atito, Zhenhua Feng, Josef Kittler, Syed Sameed Husain, Muhammad Awais, University of Surrey, United Kingdom of Great Britain and Northern Ireland
MA2.L3.2: TAXES ARE ALL YOU NEED: INTEGRATION OF TAXONOMICAL HIERARCHY RELATIONSHIPS INTO THE CONTRASTIVE LOSS
Kiran Kokilepersaud, Yavuz Yarici, Mohit Prabhushankar, Ghassan AlRegib, Georgia Institute of Technology, United States of America
MA2.L3.3: Imbalanced data robust online continual learning based on evolving class aware memory selection and built-in contrastive representation learning
rui yang, Emmanuel Dellandrea, école centrale de lyon, France; Matthieu Grard, siléane, France; Liming CHEN, école centrale de lyon, France
MA2.L3.4: Conditional Past Experience Generation for Dark Continual Learning
Cheng Feng, Chaoliang Zhong, jie wang, jun sun, Fujitsu R&D Center, Co., LTD, China; Yasuto Yokota, Fujitsu LTD, Japan
MA2.L3.5: UNSUPERVISED DOMAIN ADAPTIVE SEMANTIC SEGMENTATION BASED ON CLIP-GUIDED PROTOTYPICAL CONTRASTIVE LEARNING
Kebin Liu, Chuang Zhu, Beijing University of Posts and Telecommunications, China
Contacts