Search Results for author: Weijian Li

Found 14 papers, 4 papers with code

Scalable Semi-supervised Landmark Localization for X-ray Images using Few-shot Deep Adaptive Graph

no code implementations29 Apr 2021 Xiao-Yun Zhou, Bolin Lai, Weijian Li, Yirui Wang, Kang Zheng, Fakai Wang, ChiHung Lin, Le Lu, Lingyun Huang, Mei Han, Guotong Xie, Jing Xiao, Kuo Chang-Fu, Adam Harrison, Shun Miao

It first trains a DAG model on the labeled data and then fine-tunes the pre-trained model on the unlabeled data with a teacher-student SSL mechanism.

ConTNet: Why not use convolution and transformer at the same time?

1 code implementation27 Apr 2021 Haotian Yan, Zhe Li, Weijian Li, Changhu Wang, Ming Wu, Chuang Zhang

It is also worth pointing that, given identical strong data augmentations, the performance improvement of ConTNet is more remarkable than that of ResNet.

Image Classification Object Detection

Contour Transformer Network for One-shot Segmentation of Anatomical Structures

1 code implementation2 Dec 2020 Yuhang Lu, Kang Zheng, Weijian Li, Yirui Wang, Adam P. Harrison, ChiHung Lin, Song Wang, Jing Xiao, Le Lu, Chang-Fu Kuo, Shun Miao

In this work, we present Contour Transformer Network (CTN), a one-shot anatomy segmentation method with a naturally built-in human-in-the-loop mechanism.

One-Shot Learning One-Shot Segmentation

Blind signal decomposition of various word embeddings based on join and individual variance explained

no code implementations30 Nov 2020 Yikai Wang, Weijian Li

We found that by mapping different word embeddings into the joint component, sentiment performance can be greatly improved for the original word embeddings with lower performance.

Dimensionality Reduction Sentiment Analysis +1

Anatomy-Aware Siamese Network: Exploiting Semantic Asymmetry for Accurate Pelvic Fracture Detection in X-ray Images

no code implementations ECCV 2020 Haomin Chen, Yirui Wang, Kang Zheng, Weijian Li, Chi-Tung Cheng, Adam P. Harrison, Jing Xiao, Gregory D. Hager, Le Lu, Chien-Hung Liao, Shun Miao

A new contrastive feature learning component in our Siamese network is designed to optimize the deep image features being more salient corresponding to the underlying semantic asymmetries (caused by pelvic fracture occurrences).

Alleviating the Incompatibility between Cross Entropy Loss and Episode Training for Few-shot Skin Disease Classification

no code implementations21 Apr 2020 Wei Zhu, Haofu Liao, Wenbin Li, Weijian Li, Jiebo Luo

Inspired by the recent success of Few-Shot Learning (FSL) in natural image classification, we propose to apply FSL to skin disease identification to address the extreme scarcity of training sample problem.

Few-Shot Learning General Classification +2

Unsupervised Learning of Landmarks based on Inter-Intra Subject Consistencies

1 code implementation16 Apr 2020 Weijian Li, Haofu Liao, Shun Miao, Le Lu, Jiebo Luo

To recover from the transformed images back to the original subject, the landmark detector is forced to learn spatial locations that contain the consistent semantic meanings both for the paired intra-subject images and between the paired inter-subject images.

Uncovering Download Fraud Activities in Mobile App Markets

no code implementations5 Jul 2019 Yingtong Dou, Weijian Li, Zhirong Liu, Zhenhua Dong, Jiebo Luo, Philip S. Yu

To the best of our knowledge, this is the first work that investigates the download fraud problem in mobile App markets.

Patch Transformer for Multi-tagging Whole Slide Histopathology Images

no code implementations10 Jun 2019 Weijian Li, Viet-Duy Nguyen, Haofu Liao, Matt Wilder, Ke Cheng, Jiebo Luo

Automated whole slide image (WSI) tagging has become a growing demand due to the increasing volume and diversity of WSIs collected nowadays in histopathology.

Attentive Relational Networks for Mapping Images to Scene Graphs

no code implementations CVPR 2019 Mengshi Qi, Weijian Li, Zhengyuan Yang, Yunhong Wang, Jiebo Luo

Scene graph generation refers to the task of automatically mapping an image into a semantic structural graph, which requires correctly labeling each extracted object and their interaction relationships.

Graph Generation Object Detection +1

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