Weakly supervised structured output learning for semantic segmentation

Autor: Vittorio Ferrari, Christian Leistner, Javier Civera, Alessandro Prest, Cordelia Schmid

Publikováno v: Vezhnevets, A, Ferrari, V & Buhmann, J M 2012, Weakly supervised structured output learning for semantic segmentation . in Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on . Institute of Electrical and Electronics Engineers (IEEE), pp. 845-852 . https://doi.org/10.1109/CVPR.2012.6247757

We address the problem of weakly supervised semantic segmentation. The training images are labeled only by the classes they contain, not by their location in the image. On test images instead, the method must predict a class label for every pixel. Ou

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::539f69b298545ff043fc695991292765
https://www.pure.ed.ac.uk/ws/files/17359761/Vezhnevets_Ferrari_et_al_2012_Weakly_supervised_structured_output.pdf