TA2.L3.5
SEMI-SUPERVISED 3D OBJECT DETECTION WITH CHANNEL AUGMENTATION USING TRANSFORMATION EQUIVARIANCE
Minju Kang, KAIST, LG Electronics, Korea, Republic of; Taehun Kong, KAIST, Korea, Republic of; Tae-Kyun Kim, KAIST, Imperial College London, Korea, Republic of
Session:
TA2.L3: Augmentation for Image & Video Learning Lecture
Track:
Visual Artificial Intelligence
Location:
Lecture Room 3
Presentation Time:
Tue, 29 Oct, 11:42 - 12:00 Gulf Standard Time (UTC +4)
Session Chair:
Xiaohong Liu, Shanghai Jiao Tong University
Session TA2.L3
TA2.L3.1: ECAP: EXTENSIVE CUT-AND-PASTE AUGMENTATION FOR UNSUPERVISED DOMAIN ADAPTIVE SEMANTIC SEGMENTATION
Erik Brorsson, Volvo Group, Chalmers University of Technology, Sweden; Knut Åkesson, Lennart Svensson, Chalmers University of Technology, Sweden; Kristofer Bengtsson, Volvo Group, Sweden
TA2.L3.2: INTELLIGENT MULTI-VIEW TEST TIME AUGMENTATION
Efe Ozturk, Mohit Prabhushankar, Ghassan AlRegib, Georgia Institute of Technology, United States of America
TA2.L3.3: SUPERPIXEL MIXING: A DATA AUGMENTATION TECHNIQUE FOR ROBUST DEEP VISUAL RECOGNITION MODELS
Danyang Sun, Fadi Dornaika, University of the Basque Country, Spain; Vinh Hoang, Ho Chi Minh City Open University, Viet Nam; Nagore Barrena, University of the Basque Country, Spain
TA2.L3.4: DIVERSIFIED TASK AUGMENTATION WITH REDUNDANCY REDUCTION FOR CROSS-DOMAIN FEW-SHOT LEARNING
Ling Yue, Lin Feng, Qiuping Shuai, Lingxiao Xu, Zihao Li, Sichuan Normal University, China
TA2.L3.5: SEMI-SUPERVISED 3D OBJECT DETECTION WITH CHANNEL AUGMENTATION USING TRANSFORMATION EQUIVARIANCE
Minju Kang, KAIST, LG Electronics, Korea, Republic of; Taehun Kong, KAIST, Korea, Republic of; Tae-Kyun Kim, KAIST, Imperial College London, Korea, Republic of
Contacts