CASIA OpenIR  > 模式识别国家重点实验室  > 图像与视频分析
Weakly Supervised RBM for Semantic Segmentation
Yong Li; Jing Liu; Yuhang Wang; Hanqing Lu; Songde Ma
Conference NameInternational Joint Conference on Artificial Intelligence
Source PublicationProceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence
Conference DateJuly 25-31, 2015
Conference PlaceBuenos Aires, Argentina
AbstractIn this paper, we propose a weakly supervised Restricted Boltzmann Machines (WRBM) approach to deal with the task of semantic segmentation with only image-level labels available. In WRBM, its hidden nodes are divided into multiple blocks, and each block corresponds to a specific label. Accordingly, semantic segmentation can be directly modeled by learning the mapping from visible layer to the hidden layer of WRBM. Specifically, based on the standard RBM, we import another two terms to make full use of image-level labels and alleviate the effect of noisy labels. First, we expect the hidden response of each superpixel is suppressed on the labels outside its parent image-level label set, and a non-image-level label suppression term is formulated to implicitly import the image-level labels as weak supervision. Second, semantic graph propagation is employed to exploit the cooccurrence between visually similar regions and labels. Besides, we deal with the problems of label imbalance and diverse backgrounds by adapting the block size to the label frequency and appending hidden response blocks corresponding to backgrounds respectively. Extensive experiments on two real-world datasets demonstrate the good performance of our approach compared with some state-of-the-art methods.
Indexed ByEI
Document Type会议论文
Corresponding AuthorJing Liu
Recommended Citation
GB/T 7714
Yong Li,Jing Liu,Yuhang Wang,et al. Weakly Supervised RBM for Semantic Segmentation[C],2015.
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