39 citations · 69 across the 4 of their papers we have counts for
4 papers
Domain-Symmetric Networks for Adversarial Domain Adaptation
Yabin Zhang, Hui Tang, Kui Jia +1
Unsupervised domain adaptation aims to learn a model of classifier for unlabeled samples on the target domain, given training data of labeled samples on the source domain. Impressi…
Auto-Embedding Generative Adversarial Networks for High Resolution Image Synthesis
Yong Guo, Qi Chen, Jian Chen +3
Generating images via the generative adversarial network (GAN) has attracted much attention recently. However, most of the existing GAN-based methods can only produce low-resolutio…
You Only Look & Listen Once: Towards Fast and Accurate Visual Grounding
Chaorui Deng, Qi Wu, Guanghui Xu +4
Visual Grounding (VG) aims to locate the most relevant region in an image, based on a flexible natural language query but not a pre-defined label, thus it can be a more useful tech…
Discovering Support and Affiliated Features from Very High Dimensions
Yiteng Zhai, Mingkui Tan, Ivor Tsang +1
In this paper, a novel learning paradigm is presented to automatically identify groups of informative and correlated features from very high dimensions. Specifically, we explicitly…