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Weakly Supervised Semantic Segmentation Based on Web Image Co-segmentation
Tong Shen, Guosheng Lin, Lingqiao Liu +2
Training a Fully Convolutional Network (FCN) for semantic segmentation requires a large number of masks with pixel level labelling, which involves a large amount of human labour an…
Structured Learning of Tree Potentials in CRF for Image Segmentation
Fayao Liu, Guosheng Lin, Ruizhi Qiao +1
We propose a new approach to image segmentation, which exploits the advantages of both conditional random fields (CRFs) and decision trees. In the literature, the potential functio…
Efficient Dense Labeling of Human Activity Sequences from Wearables using Fully Convolutional Networks
Rui Yao, Guosheng Lin, Qinfeng Shi +1
Recognizing human activities in a sequence is a challenging area of research in ubiquitous computing. Most approaches use a fixed size sliding window over consecutive samples to ex…
Learning Multi-level Region Consistency with Dense Multi-label Networks for Semantic Segmentation
Tong Shen, Guosheng Lin, Chunhua Shen +1
Semantic image segmentation is a fundamental task in image understanding. Per-pixel semantic labelling of an image benefits greatly from the ability to consider region consistency…