11 citations · 38 across the 8 of their papers we have counts for
8 papers
Efficient Bilateral Cross-Modality Cluster Matching for Unsupervised Visible-Infrared Person ReID
De Cheng, Lingfeng He, Nannan Wang +3
Unsupervised visible-infrared person re-identification (USL-VI-ReID) aims to match pedestrian images of the same identity from different modalities without annotations. Existing wo…
Neighbour Consistency Guided Pseudo-Label Refinement for Unsupervised Person Re-Identification
De Cheng, Haichun Tai, Nannan Wang +2
Unsupervised person re-identification (ReID) aims at learning discriminative identity features for person retrieval without any annotations. Recent advances accomplish this task by…
MR-SVS: Singing Voice Synthesis with Multi-Reference Encoder
Shoutong Wang, Jinglin Liu, Yi Ren +3
Multi-speaker singing voice synthesis is to generate the singing voice sung by different speakers. To generalize to new speakers, previous zero-shot singing adaptation methods obta…
Overcoming Difficulty in Obtaining Dark-skinned Subjects for Remote-PPG by Synthetic Augmentation
Yunhao Ba, Zhen Wang, Kerim Doruk Karinca +2
Camera-based remote photoplethysmography (rPPG) provides a non-contact way to measure physiological signals (e.g., heart rate) using facial videos. Recent deep learning architectur…
Chinese Sentences Similarity via Cross-Attention Based Siamese Network
Zhen Wang, Xiangxie Zhang, Yicong Tan
Measuring sentence similarity is a key research area nowadays as it allows machines to better understand human languages. In this paper, we proposed a Cross-Attention Siamese Netwo…
ROBY: Evaluating the Robustness of a Deep Model by its Decision Boundaries
Jinyin Chen, Zhen Wang, Haibin Zheng +2
With the successful application of deep learning models in many real-world tasks, the model robustness becomes more and more critical. Often, we evaluate the robustness of the deep…
Cue-word Driven Neural Response Generation with a Shrinking Vocabulary
Qiansheng Wang, Yuxin Liu, Chengguo Lv +2
Open-domain response generation is the task of generating sensible and informative re-sponses to the source sentence. However, neural models tend to generate safe and mean-ingless…
Fisher-Bures Adversary Graph Convolutional Networks
Ke Sun, Piotr Koniusz, Zhen Wang
In a graph convolutional network, we assume that the graph is generated wrt some observation noise. During learning, we make small random perturbations of the graph and tr…