21 citations · 43 across the 11 of their papers we have counts for
11 papers
RLE: A Unified Perspective of Data Augmentation for Cross-Spectral Re-identification
Lei Tan, Yukang Zhang, Keke Han +4
This paper makes a step towards modeling the modality discrepancy in the cross-spectral re-identification task. Based on the Lambertain model, we observe that the non-linear modali…
Cyclic Learning: Bridging Image-level Labels and Nuclei Instance Segmentation
Yang Zhou, Yongjian Wu, Zihua Wang +5
Nuclei instance segmentation on histopathology images is of great clinical value for disease analysis. Generally, fully-supervised algorithms for this task require pixel-wise manua…
Latent Feature Relation Consistency for Adversarial Robustness
Xingbin Liu, Huafeng Kuang, Hong Liu +3
Deep neural networks have been applied in many computer vision tasks and achieved state-of-the-art performance. However, misclassification will occur when DNN predicts adversarial…
CAT:Collaborative Adversarial Training
Xingbin Liu, Huafeng Kuang, Xianming Lin +2
Adversarial training can improve the robustness of neural networks. Previous methods focus on a single adversarial training strategy and do not consider the model property trained…
Spectral Aware Softmax for Visible-Infrared Person Re-Identification
Lei Tan, Pingyang Dai, Qixiang Ye +3
Visible-infrared person re-identification (VI-ReID) aims to match specific pedestrian images from different modalities. Although suffering an extra modality discrepancy, existing m…
Exploring Invariant Representation for Visible-Infrared Person Re-Identification
Lei Tan, Yukang Zhang, Shengmei Shen +5
Cross-spectral person re-identification, which aims to associate identities to pedestrians across different spectra, faces a main challenge of the modality discrepancy. In this pap…