539 citations · 2k across the 53 of their papers we have counts for
6 papers · 1 filter
When Semi-Supervised Learning Meets Transfer Learning: Training Strategies, Models and Datasets
Hong-Yu Zhou, Avital Oliver, Jianxin Wu +1
Semi-Supervised Learning (SSL) has been proved to be an effective way to leverage both labeled and unlabeled data at the same time. Recent semi-supervised approaches focus on deep…
Coarse-to-fine: A RNN-based hierarchical attention model for vehicle re-identification
Xiu-Shen Wei, Chen-Lin Zhang, Lingqiao Liu +2
Vehicle re-identification is an important problem and becomes desirable with the rapid expansion of applications in video surveillance and intelligent transportation. By recalling…
AutoPruner: An End-to-End Trainable Filter Pruning Method for Efficient Deep Model Inference
Jian-Hao Luo, Jianxin Wu
Channel pruning is an important family of methods to speed up deep model's inference. Previous filter pruning algorithms regard channel pruning and model fine-tuning as two indepen…
Piecewise classifier mappings: Learning fine-grained learners for novel categories with few examples
Xiu-Shen Wei, Peng Wang, Lingqiao Liu +2
Humans are capable of learning a new fine-grained concept with very little supervision, \emph{e.g.}, few exemplary images for a species of bird, yet our best deep learning systems…
Vortex Pooling: Improving Context Representation in Semantic Segmentation
Chen-Wei Xie, Hong-Yu Zhou, Jianxin Wu
Semantic segmentation is a fundamental task in computer vision, which can be considered as a per-pixel classification problem. Recently, although fully convolutional neural network…
Learning Effective Binary Visual Representations with Deep Networks
Jianxin Wu, Jian-Hao Luo
Although traditionally binary visual representations are mainly designed to reduce computational and storage costs in the image retrieval research, this paper argues that binary vi…