公开(公告)号：EP4451230A1

公开(公告)日：2024-10-23

申请号：EP23168202.2

申请日：2023-04-17

申请人： TOYOTA JIDOSHA KABUSHIKI KAISHA ,  Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V.

发明人： OLMEDA REINO, Daniel ,  DAS, Anurag ,  DAI, Dengxin ,  SCHIELE, Bernt ,  XIAN, Yongqin

IPC分类号： G06V10/774 ,  G06V10/26

摘要： The present invention relates to a method for training an image segmentation model (ISM) for segmenting images (IMG) of a second type, the model (ISM) having been trained (T100) using a first set ( X s ) of images of a first type and having labels ( Y s ), the method comprising training (T200) the model (ISM) using at least a second set ( X t ) of images of the second type and having weak-labels ( Y t ), the weakly-labeled images comprising unlabeled pixels, wherein said training (T200) is performed using a loss function ( ) based on: similarity measures ( f t i ⋅ η t k ) between class prototypes ( η t k ) and features ( f t i ) of the images of the second set ( X t ); and on the weak-labels ( y t i k ) of the images of the second set ( X t ).