17 citations · 32 across the 8 of their papers we have counts for
4 papers · 1 filter
BP-Triplet Net for Unsupervised Domain Adaptation: A Bayesian Perspective
Shanshan Wang, Lei Zhang, Pichao Wang
Triplet loss, one of the deep metric learning (DML) methods, is to learn the embeddings where examples from the same class are closer than examples from different classes. Motivate…
Bounding boxes for weakly supervised segmentation: Global constraints get close to full supervision
Hoel Kervadec, Jose Dolz, Shanshan Wang +2
We propose a novel weakly supervised learning segmentation based on several global constraints derived from box annotations. Particularly, we leverage a classical tightness prior t…
Manifold Criterion Guided Transfer Learning via Intermediate Domain Generation
Lei Zhang, Shanshan Wang, Guang-Bin Huang +3
In many practical transfer learning scenarios, the feature distribution is different across the source and target domains (i.e. non-i.i.d.). Maximum mean discrepancy (MMD), as a do…
Fine-grained visual recognition with salient feature detection
Hui Feng, Shanshan Wang, Shuzhi Sam Ge
Computer vision based fine-grained recognition has received great attention in recent years. Existing works focus on discriminative part localization and feature learning. In this…