output
20052024
most citedRandom Erasing Data Augmentation

748 citations

Showing cs.CVShow all

55 papers · 1 filter

cs.CV20224 cited

Learning Shape Priors by Pairwise Comparison for Robust Semantic Segmentation

Cong Xie, Hualuo Liu, Shilei Cao +4

Semantic segmentation is important in medical image analysis. Inspired by the strong ability of traditional image analysis techniques in capturing shape priors and inter-subject si…

cs.CV202266 cited

Towards Lightweight Transformer via Group-wise Transformation for Vision-and-Language Tasks

Gen Luo, Yiyi Zhou, Xiaoshuai Sun +5

Despite the exciting performance, Transformer is criticized for its excessive parameters and computation cost. However, compressing Transformer remains as an open problem due to it…

cs.CV202243 cited

Deep Multi-Branch Aggregation Network for Real-Time Semantic Segmentation in Street Scenes

Xi Weng, Yan Yan, Genshun Dong +4

Real-time semantic segmentation, which aims to achieve high segmentation accuracy at real-time inference speed, has received substantial attention over the past few years. However,…

cs.CV202266 cited

Stage-Aware Feature Alignment Network for Real-Time Semantic Segmentation of Street Scenes

Xi Weng, Yan Yan, Si Chen +2

Over the past few years, deep convolutional neural network-based methods have made great progress in semantic segmentation of street scenes. Some recent methods align feature maps…

cs.CV202113 cited

Unsupervised Representation Learning Meets Pseudo-Label Supervised Self-Distillation: A New Approach to Rare Disease Classification

Jinghan Sun, Dong Wei, Kai Ma +2

Rare diseases are characterized by low prevalence and are often chronically debilitating or life-threatening. Imaging-based classification of rare diseases is challenging due to th…

cs.CV202113 cited

Efficient Global-Local Memory for Real-time Instrument Segmentation of Robotic Surgical Video

Jiacheng Wang, Yueming Jin, Liansheng Wang +3

Performing a real-time and accurate instrument segmentation from videos is of great significance for improving the performance of robotic-assisted surgery. We identify two importan…