373 citations
- NEC (Japan)JP12 papers
- Michigan State UniversityUS7 papers
- Princeton UniversityUS7 papers
- University of California, MercedUS7 papers
- University of California San DiegoUS7 papers
- Rutgers, The State University of New JerseyUS4 papers
- Stanford UniversityUS4 papers
- Case Western Reserve UniversityUS3 papers
- Columbia UniversityUS3 papers
- The University of TokyoJP3 papers
- Université Paris-SudFR3 papers
- University of WaterlooCA3 papers
5 papers · 2 filters
Scalable Deep -Subspace Clustering
Tong Zhang, Pan Ji, Mehrtash Harandi +2
Subspace clustering algorithms are notorious for their scalability issues because building and processing large affinity matrices are demanding. In this paper, we introduce a metho…
Fast and Accurate Online Video Object Segmentation via Tracking Parts
Jingchun Cheng, Yi-Hsuan Tsai, Wei-Chih Hung +2
Online video object segmentation is a challenging task as it entails to process the image sequence timely and accurately. To segment a target object through the video, numerous CNN…
Gotta Adapt 'Em All: Joint Pixel and Feature-Level Domain Adaptation for Recognition in the Wild
Luan Tran, Kihyuk Sohn, Xiang Yu +2
Recent developments in deep domain adaptation have allowed knowledge transfer from a labeled source domain to an unlabeled target domain at the level of intermediate features or in…
Adversarial Learning for Semi-Supervised Semantic Segmentation
Wei-Chih Hung, Yi-Hsuan Tsai, Yan-Ting Liou +2
We propose a method for semi-supervised semantic segmentation using an adversarial network. While most existing discriminators are trained to classify input images as real or fake…
Learning random-walk label propagation for weakly-supervised semantic segmentation
Paul Vernaza, Manmohan Chandraker
Large-scale training for semantic segmentation is challenging due to the expense of obtaining training data for this task relative to other vision tasks. We propose a novel trainin…