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Baocai Yin
Researcher at Dalian University of Technology
Publications - 174
Citations - 3527
Baocai Yin is an academic researcher from Dalian University of Technology. The author has contributed to research in topics: Computer science & Pattern recognition (psychology). The author has an hindex of 21, co-authored 69 publications receiving 2505 citations. Previous affiliations of Baocai Yin include Beijing University of Technology & Beijing Institute of Technology.
Papers
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Proceedings ArticleDOI
Learning to Detect Salient Objects with Image-Level Supervision
TL;DR: This paper develops a weakly supervised learning method for saliency detection using image-level tags only, which outperforms unsupervised ones with a large margin, and achieves comparable or even superior performance than fully supervised counterparts.
Proceedings ArticleDOI
Learning Uncertain Convolutional Features for Accurate Saliency Detection
TL;DR: A novel deep fully convolutional network model for accurate salient object detection and an effective hybrid upsampling method to reduce the checkerboard artifacts of deconvolution operators in the authors' decoder network are proposed.
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Learning Uncertain Convolutional Features for Accurate Saliency Detection
TL;DR: Zhang et al. as discussed by the authors proposed a deep fully convolutional network model for accurate salient object detection, which is able to incorporate uncertainties for more accurate object boundary inference by introducing a reformulated dropout (R-dropout).
Proceedings ArticleDOI
Data gathering in wireless sensor networks through intelligent compressive sensing
TL;DR: An adaptive data gathering scheme by compressive sensing by introducing autoregressive (AR) model into the reconstruction of the sensed data, the local correlation in sensed data is exploited and thus local adaptive sparsity is achieved.
Journal ArticleDOI
A hierarchical multiscale and multiangle system for human face detection in a complex background using gravity-center template
TL;DR: A novel faster search scheme of gravity-center template matching compared with the traditional search method in an image for human face detection, which significantly saves the time consumed in rough detection of human faces in a mosaic image.