output
20062026
most citedThe Medical Segmentation Decathlon

1.3k citations

Showing 2019 · cs.CVShow all

48 papers · 2 filters

cs.CV2019★ 3 cited

Learning Semantic Neural Tree for Human Parsing

Ruyi Ji, Dawei Du, Libo Zhang +5

The majority of existing human parsing methods formulate the task as semantic segmentation, which regard each semantic category equally and fail to exploit the intrinsic physiologi…

cs.CV2019★ 2 cited

Coupled Network for Robust Pedestrian Detection with Gated Multi-Layer Feature Extraction and Deformable Occlusion Handling

Tianrui Liu, Wenhan Luo, Lin Ma +3

Pedestrian detection methods have been significantly improved with the development of deep convolutional neural networks. Nevertheless, detecting small-scaled pedestrians and occlu…

cs.CV2019★ 13 cited

Shape-Aware Organ Segmentation by Predicting Signed Distance Maps

Yuan Xue, Hui Tang, Zhi Qiao +6

In this work, we propose to resolve the issue existing in current deep learning based organ segmentation systems that they often produce results that do not capture the overall sha…

cs.CV2019★ 13 cited

Asymmetric Co-Teaching for Unsupervised Cross Domain Person Re-Identification

Fengxiang Yang, Ke Li, Zhun Zhong +7

Person re-identification (re-ID), is a challenging task due to the high variance within identity samples and imaging conditions. Although recent advances in deep learning have achi…

cs.CV2019★ 8 cited

Pyramid Multi-view Stereo Net with Self-adaptive View Aggregation

Hongwei Yi, Zizhuang Wei, Mingyu Ding +4

n this paper, we propose an effective and efficient pyramid multi-view stereo (MVS) net with self-adaptive view aggregation for accurate and complete dense point cloud reconstructi…

cs.CV2019★ 1 cited

Rethinking Temporal Fusion for Video-based Person Re-identification on Semantic and Time Aspect

Xinyang Jiang, Yifei Gong, Xiaowei Guo +5

Recently, the research interest of person re-identification (ReID) has gradually turned to video-based methods, which acquire a person representation by aggregating frame features…