TA-L.C1: Machine Learning for Image & Video Analysis
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 C1
Session Chair: Anthony Vetro, Mitsubishi Electric Research Labs
Track: Applications of Machine Learning

TA-L.C1.1: BACKGROUND-TOLERANT OBJECT CLASSIFICATION WITH EMBEDDED SEGMENTATION MASK FOR INFRARED AND COLOR IMAGERY
Maliha Arif, Calvin Yong, Abhijit Mahalanobis, Nazanin Rahnavard, University of Central Florida, United States of America

TA-L.C1.2: MULTI-SCALE RAFT: COMBINING HIERARCHICAL CONCEPTS FOR LEARNING-BASED OPTICAL FLOW ESTIMATION
Azin Jahedi, Lukas Mehl, Marc Rivinius, Andrés Bruhn, University of Stuttgart, Germany

TA-L.C1.3: MONO6D: MONOCULAR VEHICLE 6D POSE ESTIMATION WITH 3D PRIORS
Yangxintong Lyu, Remco Royen, Adrian Munteanu, Vrije Universiteit Brussel, Belgium

TA-L.C1.4: PREDICTING HUMAN PERCEPTION OF SCENE COMPLEXITY
Cameron Kyle-Davidson, Adrian G. Bors, Karla K. Evans, University of York, United Kingdom of Great Britain and Northern Ireland

TA-L.C1.5: DOMAIN ADAPTATION FOR UNKNOWN IMAGE DISTORTIONS IN INSTANCE SEGMENTATION
Maximiliane Gruber, Fabian Brand, Alina Mosebach, Jürgen Seiler, André Kaup, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Germany

TA-L.C1.6: ON QUANTIZATION OF IMAGE CLASSIFICATION NEURAL NETWORKS FOR COMPRESSION WITHOUT RETRAINING
Marcos Tonin, Ricardo de Queiroz, Universidade de Brasilia, Brazil