Search Results for author: Hong-Yu Zhou

Found 19 papers, 9 papers with code

Preservational Learning Improves Self-supervised Medical Image Models by Reconstructing Diverse Contexts

1 code implementation9 Sep 2021 Hong-Yu Zhou, Chixiang Lu, Sibei Yang, Xiaoguang Han, Yizhou Yu

From this perspective, we introduce Preservational Learning to reconstruct diverse image contexts in order to preserve more information in learned representations.

Contrastive Learning Representation Learning +1

nnFormer: Interleaved Transformer for Volumetric Segmentation

1 code implementation7 Sep 2021 Hong-Yu Zhou, Jiansen Guo, Yinghao Zhang, Lequan Yu, Liansheng Wang, Yizhou Yu

Compared to the naive voxel-level self-attention implementation, such volume-based operations help to reduce the computational complexity by approximate 98% and 99. 5% on Synapse and ACDC datasets, respectively.

Medical Image Segmentation

ConvNets vs. Transformers: Whose Visual Representations are More Transferable?

no code implementations11 Aug 2021 Hong-Yu Zhou, Chixiang Lu, Sibei Yang, Yizhou Yu

Vision transformers have attracted much attention from computer vision researchers as they are not restricted to the spatial inductive bias of ConvNets.

Classification Depth Estimation +3

Bottom-Up Shift and Reasoning for Referring Image Segmentation

1 code implementation CVPR 2021 Sibei Yang, Meng Xia, Guanbin Li, Hong-Yu Zhou, Yizhou Yu

In this paper, we tackle the challenge by jointly performing compositional visual reasoning and accurate segmentation in a single stage via the proposed novel Bottom-Up Shift (BUS) and Bidirectional Attentive Refinement (BIAR) modules.

Semantic Segmentation Visual Reasoning

Generalized Organ Segmentation by Imitating One-shot Reasoning using Anatomical Correlation

no code implementations30 Mar 2021 Hong-Yu Zhou, Hualuo Liu, Shilei Cao, Dong Wei, Chixiang Lu, Yizhou Yu, Kai Ma, Yefeng Zheng

In this paper, we show that such process can be integrated into the one-shot segmentation task which is a very challenging but meaningful topic.

One-Shot Segmentation

MixSearch: Searching for Domain Generalized Medical Image Segmentation Architectures

1 code implementation26 Feb 2021 Luyan Liu, Zhiwei Wen, Songwei Liu, Hong-Yu Zhou, Hongwei Zhu, Weicheng Xie, Linlin Shen, Kai Ma, Yefeng Zheng

Considering the scarcity of medical data, most datasets in medical image analysis are an order of magnitude smaller than those of natural images.

Medical Image Segmentation

Difficulty-aware Glaucoma Classification with Multi-Rater Consensus Modeling

no code implementations29 Jul 2020 Shuang Yu, Hong-Yu Zhou, Kai Ma, Cheng Bian, Chunyan Chu, Hanruo Liu, Yefeng Zheng

However, when being used for model training, only the final ground-truth label is utilized, while the critical information contained in the raw multi-rater gradings regarding the image being an easy/hard case is discarded.

Classification General Classification

A Macro-Micro Weakly-supervised Framework for AS-OCT Tissue Segmentation

no code implementations20 Jul 2020 Munan Ning, Cheng Bian, Donghuan Lu, Hong-Yu Zhou, Shuang Yu, Chenglang Yuan, Yang Guo, Yaohua Wang, Kai Ma, Yefeng Zheng

Primary angle closure glaucoma (PACG) is the leading cause of irreversible blindness among Asian people.

Comparing to Learn: Surpassing ImageNet Pretraining on Radiographs By Comparing Image Representations

1 code implementation15 Jul 2020 Hong-Yu Zhou, Shuang Yu, Cheng Bian, Yifan Hu, Kai Ma, Yefeng Zheng

In deep learning era, pretrained models play an important role in medical image analysis, in which ImageNet pretraining has been widely adopted as the best way.

Learning Expectation of Label Distribution for Facial Age and Attractiveness Estimation

1 code implementation3 Jul 2020 Bin-Bin Gao, Xin-Xin Liu, Hong-Yu Zhou, Jianxin Wu, Xin Geng

Our method achieves new state-of-the-art results using the single model with 36$\times$(6$\times$) fewer parameters and 2. 6$\times$(2. 1$\times$) faster inference speed on facial age (attractiveness) estimation.

Age Estimation Attractiveness Estimation

Learning to Discover Multi-Class Attentional Regions for Multi-Label Image Recognition

1 code implementation3 Jul 2020 Bin-Bin Gao, Hong-Yu Zhou

To bridge the gap between global and local streams, we propose a multi-class attentional region module which aims to make the number of attentional regions as small as possible and keep the diversity of these regions as high as possible.

Multi-Label Classification

When Semi-Supervised Learning Meets Transfer Learning: Training Strategies, Models and Datasets

no code implementations13 Dec 2018 Hong-Yu Zhou, Avital Oliver, Jianxin Wu, Yefeng Zheng

While practitioners have had an intuitive understanding of these observations, we do a comprehensive emperical analysis and demonstrate that: (1) the gains from SSL techniques over a fully-supervised baseline are smaller when trained from a pre-trained model than when trained from random initialization, (2) when the domain of the source data used to train the pre-trained model differs significantly from the domain of the target task, the gains from SSL are significantly higher and (3) some SSL methods are able to advance fully-supervised baselines (like Pseudo-Label).

Transfer Learning

Age Estimation Using Expectation of Label Distribution Learning

1 code implementation13 Jul 2018 Bin-Bin Gao, Hong-Yu Zhou, Jianxin Wu, Xin Geng

Age estimation performance has been greatly improved by using convolutional neural network.

Age Estimation Face Recognition +1

Vortex Pooling: Improving Context Representation in Semantic Segmentation

no code implementations17 Apr 2018 Chen-Wei Xie, Hong-Yu Zhou, Jianxin Wu

To be specific, our approach outperforms the previous state-of-the-art model named DeepLab v3 by 1. 5% on the PASCAL VOC 2012 val set and 0. 6% on the test set by replacing the Atrous Spatial Pyramid Pooling (ASPP) module in DeepLab v3 with the proposed Vortex Pooling.

Semantic Segmentation

Code Attention: Translating Code to Comments by Exploiting Domain Features

2 code implementations22 Sep 2017 Wenhao Zheng, Hong-Yu Zhou, Ming Li, Jianxin Wu

Appropriate comments of code snippets provide insight for code functionality, which are helpful for program comprehension.

Adaptive Feeding: Achieving Fast and Accurate Detections by Adaptively Combining Object Detectors

no code implementations ICCV 2017 Hong-Yu Zhou, Bin-Bin Gao, Jianxin Wu

In this paper, we propose Adaptive Feeding (AF) to combine a fast (but less accurate) detector and an accurate (but slow) detector, by adaptively determining whether an image is easy or hard and choosing an appropriate detector for it.

Object Detection

Sunrise or Sunset: Selective Comparison Learning for Subtle Attribute Recognition

no code implementations20 Jul 2017 Hong-Yu Zhou, Bin-Bin Gao, Jianxin Wu

The difficulty of image recognition has gradually increased from general category recognition to fine-grained recognition and to the recognition of some subtle attributes such as temperature and geolocation.

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