45 citations · 52 across the 5 of their papers we have counts for
8 papers
CRNet: Cross-Reference Networks for Few-Shot Segmentation
Weide Liu, Chi Zhang, Guosheng Lin +1
Over the past few years, state-of-the-art image segmentation algorithms are based on deep convolutional neural networks. To render a deep network with the ability to understand a c…
Towards Robust Curve Text Detection with Conditional Spatial Expansion
Zichuan Liu, Guosheng Lin, Sheng Yang +3
It is challenging to detect curve texts due to their irregular shapes and varying sizes. In this paper, we first investigate the deficiency of the existing curve detection methods…
CANet: Class-Agnostic Segmentation Networks with Iterative Refinement and Attentive Few-Shot Learning
Chi Zhang, Guosheng Lin, Fayao Liu +2
Recent progress in semantic segmentation is driven by deep Convolutional Neural Networks and large-scale labeled image datasets. However, data labeling for pixel-wise segmentation…
Correlation Propagation Networks for Scene Text Detection
Zichuan Liu, Guosheng Lin, Wang Ling Goh +3
In this work, we propose a novel hybrid method for scene text detection namely Correlation Propagation Network (CPN). It is an end-to-end trainable framework engined by advanced Co…
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…
Structured Learning of Binary Codes with Column Generation
Guosheng Lin, Fayao Liu, Chunhua Shen +2
Hashing methods aim to learn a set of hash functions which map the original features to compact binary codes with similarity preserving in the Hamming space. Hashing has proven a v…