activity
20202022
most citedAnalysis on DeepLabV3+ Performance for Automatic Steel Defects Detection

6 citations · 10 across the 5 of their papers we have counts for

collaborators

5 papers

eess.IV2022

Holistic Fine-grained GGS Characterization: From Detection to Unbalanced Classification

Yuzhe Lu, Haichun Yang, Zuhayr Asad +5

Recent studies have demonstrated the diagnostic and prognostic values of global glomerulosclerosis (GGS) in IgA nephropathy, aging, and end-stage renal disease. However, the fine-g…

cs.CV20213 cited

Omni-supervised Point Cloud Segmentation via Gradual Receptive Field Component Reasoning

Jingyu Gong, Jiachen Xu, Xin Tan +4

Hidden features in neural network usually fail to learn informative representation for 3D segmentation as supervisions are only given on output prediction, while this can be solved…

cs.CV2021

Boundary-Aware Geometric Encoding for Semantic Segmentation of Point Clouds

Jingyu Gong, Jiachen Xu, Xin Tan +4

Boundary information plays a significant role in 2D image segmentation, while usually being ignored in 3D point cloud segmentation where ambiguous features might be generated in fe…

cs.CV20206 cited

Analysis on DeepLabV3+ Performance for Automatic Steel Defects Detection

Zheng Nie, Jiachen Xu, Shengchang Zhang

Our works experimented DeepLabV3+ with different backbones on a large volume of steel images aiming to automatically detect different types of steel defects. Our methods applied ra…

cs.CV20201 cited

SceneEncoder: Scene-Aware Semantic Segmentation of Point Clouds with A Learnable Scene Descriptor

Jiachen Xu, Jingyu Gong, Jie Zhou +3

Besides local features, global information plays an essential role in semantic segmentation, while recent works usually fail to explicitly extract the meaningful global information…