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
Session:
Machine Learning for Image & Video Analysis
Track:
Applications of Machine Learning
Location:
Room C1
Presentation Time:
Tue, 18 Oct, 17:15 - 17:30 China Standard Time (UTC +8)
Tue, 18 Oct, 11:15 - 11:30 Central European Time (UTC +1)
Tue, 18 Oct, 09:15 - 09:30 UTC
Tue, 18 Oct, 05:15 - 05:30 Eastern Time (UTC -5)
Tue, 18 Oct, 11:15 - 11:30 Central European Time (UTC +1)
Tue, 18 Oct, 09:15 - 09:30 UTC
Tue, 18 Oct, 05:15 - 05:30 Eastern Time (UTC -5)
Session Chair:
Anthony Vetro, Mitsubishi Electric Research Labs
Presentation
Discussion
Resources
No resources available.
Session TA-L.C1
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