MA-L.C1.2
LEARNING TRAJECTORY-CONDITIONED RELATIONS TO PREDICT PEDESTRIAN CROSSING BEHAVIOR
Chen Zhou, Ghassan AlRegib, Georgia Institute of Technology, United States of America; Armin Parchami, Kunjan Singh, Ford Motor Company, United States of America
Session:
Machine Learning for Image & Video Sensing
Track:
Applications of Machine Learning
Location:
Room C1
Presentation Time:
Mon, 17 Oct, 16:45 - 17:00 China Standard Time (UTC +8)
Mon, 17 Oct, 10:45 - 11:00 Central European Time (UTC +2)
Mon, 17 Oct, 08:45 - 09:00 UTC
Mon, 17 Oct, 04:45 - 05:00 Eastern Time (UTC -4)
Mon, 17 Oct, 10:45 - 11:00 Central European Time (UTC +2)
Mon, 17 Oct, 08:45 - 09:00 UTC
Mon, 17 Oct, 04:45 - 05:00 Eastern Time (UTC -4)
Session Chair:
Moncef Gabbouj, Tampere University
Presentation
Discussion
Resources
No resources available.
Session MA-L.C1
MA-L.C1.1: A STUDY OF SHAPE MODELING AGAINST NOISE
Cheng Long, Adrian Barbu, Florida State University, United States of America
MA-L.C1.2: LEARNING TRAJECTORY-CONDITIONED RELATIONS TO PREDICT PEDESTRIAN CROSSING BEHAVIOR
Chen Zhou, Ghassan AlRegib, Georgia Institute of Technology, United States of America; Armin Parchami, Kunjan Singh, Ford Motor Company, United States of America
MA-L.C1.3: OBJECT-CENTRIC AND MEMORY-GUIDED NORMALITY RECONSTRUCTION FOR VIDEO ANOMALY DETECTION
Khalil Bergaoui, Yassine Naji, Aleksandr Setkov, Angélique Loesch, Romaric Audigier, CEA, France; Michèle Gouiffès, CNRS, France
MA-L.C1.4: EXPLOITING SPATIAL SPARSITY FOR EVENT CAMERAS WITH VISUAL TRANSFORMERS
Zuowen Wang, Yuhuang Hu, Shih-Chii Liu, University of Zürich and ETH Zürich, Switzerland
MA-L.C1.5: TRANSFORMERS FOR WORKOUT VIDEO SEGMENTATION
Bruno Ferreira, Paulo Menezes, Jorge Batista, University of Coimbra, Portugal