Search Results for author: Jiachen Xu

Found 10 papers, 6 papers with code

Prototype-Aware Heterogeneous Task for Point Cloud Completion

no code implementations5 Sep 2022 Junshu Tang, Jiachen Xu, Jingyu Gong, Haichuan Song, Yuan Xie, Lizhuang Ma

Moreover, for effective training, we consider difficulty-based sampling strategy to encourage the network to pay more attention to some partial point clouds with fewer geometric information.

Point Cloud Completion

Compound Figure Separation of Biomedical Images: Mining Large Datasets for Self-supervised Learning

1 code implementation30 Aug 2022 Tianyuan Yao, Chang Qu, Jun Long, Quan Liu, Ruining Deng, Yuanhan Tian, Jiachen Xu, Aadarsh Jha, Zuhayr Asad, Shunxing Bao, Mengyang Zhao, Agnes B. Fogo, Bennett A. Landman, Haichun Yang, Catie Chang, Yuankai Huo

In order to extract and separate compound figures into usable individual images for downstream learning, we propose a simple compound figure separation (SimCFS) framework without using the traditionally required detection bounding box annotations, with a new loss function and a hard case simulation.

Contrastive Learning Image Augmentation +2

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

1 code implementation31 Jan 2022 Yuzhe Lu, Haichun Yang, Zuhayr Asad, Zheyu Zhu, Tianyuan Yao, Jiachen Xu, Agnes B. Fogo, Yuankai Huo

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


Compound Figure Separation of Biomedical Images with Side Loss

1 code implementation19 Jul 2021 Tianyuan Yao, Chang Qu, Quan Liu, Ruining Deng, Yuanhan Tian, Jiachen Xu, Aadarsh Jha, Shunxing Bao, Mengyang Zhao, Agnes B. Fogo, Bennett A. Landman, Catie Chang, Haichun Yang, Yuankai Huo

Our technical contribution is three-fold: (1) we introduce a new side loss that is designed for compound figure separation; (2) we introduce an intra-class image augmentation method to simulate hard cases; (3) the proposed framework enables an efficient deployment to new classes of images, without requiring resource extensive bounding box annotations.

Contrastive Learning Image Augmentation +1

Boundary-Aware Geometric Encoding for Semantic Segmentation of Point Clouds

no code implementations7 Jan 2021 Jingyu Gong, Jiachen Xu, Xin Tan, Jie zhou, Yanyun Qu, Yuan Xie, Lizhuang Ma

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 feature extraction, leading to misclassification in the transition area between two objects.

Image Segmentation Point Cloud Segmentation +1

Analysis on DeepLabV3+ Performance for Automatic Steel Defects Detection

no code implementations9 Apr 2020 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.

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

1 code implementation24 Jan 2020 Jiachen Xu, Jingyu Gong, Jie zhou, Xin Tan, Yuan Xie, Lizhuang Ma

Besides local features, global information plays an essential role in semantic segmentation, while recent works usually fail to explicitly extract the meaningful global information and make full use of it.

Segmentation Semantic Segmentation

Cannot find the paper you are looking for? You can Submit a new open access paper.