MCL member, Ye Wang presented Qin Huang’s paper at Winter Conference on Applications of Computer Vision (WACV) 2018, Lake Tahoe, NV/CA
The title of Qin’s paper is “Unsupervised Clustering Guided Semantic Segmentation”, with Chunyang Xia, Siyang Li, Ye Wang, Yuhang Song and C.-C. Jay Kuo as the co-authors. Here is a brief summary:
“With the development of Fully Convolutional Neural Network (FCN), there have been progressive advances in the field of semantic segmentation in recent years. The FCN-based solutions are able to summarize features across training images and generate matching templates for the desired object classes, yet they overlook intra-class difference (ICD) among multiple instances in the same class. In this work, we present a novel fine-to-coarse learning (FCL) procedure, which first guides the network with designed ‘finer’ sub-class labels, whose decisions are mapped to the original ‘coarse’ object category through end-to-end learning. A sub-class labeling strategy is designed with unsupervised clustering upon deep convolutional features, and the proposed FCL procedure enables a balance between the fine-scale (i.e. sub-class) and the coarse-scale (i.e. class) knowledge. We conduct extensive experiments on several popular datasets, including PASCAL VOC, Context, Person-Part and NYUDepth-v2 to demonstrate the advantage of learning finer sub-classes and the potential to guide the learning of deep networks with unsupervised clustering.”
Congratulations to Qin Huang for his successful presentation at WACV!
