272 citations · 834 across the 20 of their papers we have counts for
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RSG: A Simple but Effective Module for Learning Imbalanced Datasets
Jianfeng Wang, Thomas Lukasiewicz, Xiaolin Hu +2
Imbalanced datasets widely exist in practice and area great challenge for training deep neural models with agood generalization on infrequent classes. In this work, wepropose a new…
Convolutional Neural Networks with Gated Recurrent Connections
Jianfeng Wang, Xiaolin Hu
The convolutional neural network (CNN) has become a basic model for solving many computer vision problems. In recent years, a new class of CNNs, recurrent convolution neural networ…
RefineMask: Towards High-Quality Instance Segmentation with Fine-Grained Features
Gang Zhang, Xin Lu, Jingru Tan +4
The two-stage methods for instance segmentation, e.g. Mask R-CNN, have achieved excellent performance recently. However, the segmented masks are still very coarse due to the downsa…
Look Closer to Segment Better: Boundary Patch Refinement for Instance Segmentation
Chufeng Tang, Hang Chen, Xiao Li +3
Tremendous efforts have been made on instance segmentation but the mask quality is still not satisfactory. The boundaries of predicted instance masks are usually imprecise due to t…
CloudAAE: Learning 6D Object Pose Regression with On-line Data Synthesis on Point Clouds
Ge Gao, Mikko Lauri, Xiaolin Hu +2
It is often desired to train 6D pose estimation systems on synthetic data because manual annotation is expensive. However, due to the large domain gap between the synthetic and rea…
Rethinking Natural Adversarial Examples for Classification Models
Xiao Li, Jianmin Li, Ting Dai +3
Recently, it was found that many real-world examples without intentional modifications can fool machine learning models, and such examples are called "natural adversarial examples"…