activity
20172023
most citedPatch-based Output Space Adversarial Learning for Joint Optic Disc and Cup Segmentation

316 citations · 797 across the 42 of their papers we have counts for

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Showing 2019 · cs.CVShow all

15 papers · 2 filters

cs.CV2019

Instance Shadow Detection

Tianyu Wang, Xiaowei Hu, Qiong Wang +2

Instance shadow detection is a brand new problem, aiming to find shadow instances paired with object instances. To approach it, we first prepare a new dataset called SOBA, named af…

cs.CV2019

Revisiting Shadow Detection: A New Benchmark Dataset for Complex World

Xiaowei Hu, Tianyu Wang, Chi-Wing Fu +3

Shadow detection in general photos is a nontrivial problem, due to the complexity of the real world. Though recent shadow detectors have already achieved remarkable performance on…

cs.CV2019★ 32 cited

Hierarchical Point-Edge Interaction Network for Point Cloud Semantic Segmentation

Li Jiang, Hengshuang Zhao, Shu Liu +3

We achieve 3D semantic scene labeling by exploring semantic relation between each point and its contextual neighbors through edges. Besides an encoder-decoder branch for predicting…

cs.CV2019

Deep Floor Plan Recognition Using a Multi-Task Network with Room-Boundary-Guided Attention

Zhiliang Zeng, Xianzhi Li, Ying Kin Yu +1

This paper presents a new approach to recognize elements in floor plan layouts. Besides walls and rooms, we aim to recognize diverse floor plan elements, such as doors, windows and…

cs.CV2019

Boundary and Entropy-driven Adversarial Learning for Fundus Image Segmentation

Shujun Wang, Lequan Yu, Kang Li +3

Accurate segmentation of the optic disc (OD) and cup (OC)in fundus images from different datasets is critical for glaucoma disease screening. The cross-domain discrepancy (domain s…

cs.CV2019

PU-GAN: a Point Cloud Upsampling Adversarial Network

Ruihui Li, Xianzhi Li, Chi-Wing Fu +2

Point clouds acquired from range scans are often sparse, noisy, and non-uniform. This paper presents a new point cloud upsampling network called PU-GAN, which is formulated based o…