Search Results for author: Wei Ke

Found 12 papers, 7 papers with code

Leverage Your Local and Global Representations: A New Self-Supervised Learning Strategy

no code implementations CVPR 2022 Tong Zhang, Congpei Qiu, Wei Ke, Sabine Süsstrunk, Mathieu Salzmann

In essence, this strategy ignores the fact that two crops may truly contain different image information, e. g., background and small objects, and thus tends to restrain the diversity of the learned representations.

Self-Supervised Learning Transfer Learning

M2MRF: Many-to-Many Reassembly of Features for Tiny Lesion Segmentation in Fundus Images

1 code implementation30 Oct 2021 Qing Liu, Haotian Liu, Wei Ke, Yixiong Liang

It reassembles features in a dimension-reduced feature space and simultaneously aggregates multiple features inside a large predefined region into multiple target features.

Lesion Segmentation

Discovery-and-Selection: Towards Optimal Multiple Instance Learning for Weakly Supervised Object Detection

no code implementations18 Oct 2021 Shiwei Zhang, Wei Ke, Lin Yang

Weakly supervised object detection (WSOD) is a challenging task that requires simultaneously learn object classifiers and estimate object locations under the supervision of image category labels.

Multiple Instance Learning object-detection +1

A Multiple Classifier Approach for Concatenate-Designed Neural Networks

no code implementations14 Jan 2021 Ka-Hou Chan, Sio-Kei Im, Wei Ke

This article introduces a multiple classifier method to improve the performance of concatenate-designed neural networks, such as ResNet and DenseNet, with the purpose to alleviate the pressure on the final classifier.

General Classification

Multiple Anchor Learning for Visual Object Detection

3 code implementations CVPR 2020 Wei Ke, Tianliang Zhang, Zeyi Huang, Qixiang Ye, Jianzhuang Liu, Dong Huang

In this paper, we propose a Multiple Instance Learning (MIL) approach that selects anchors and jointly optimizes the two modules of a CNN-based object detector.

General Classification Multiple Instance Learning +2

DDNet: Cartesian-polar Dual-domain Network for the Joint Optic Disc and Cup Segmentation

no code implementations18 Apr 2019 Qing Liu, Xiaopeng Hong, Wei Ke, Zailiang Chen, Beiji Zou

In this paper, we propose a novel segmentation approach, named Cartesian-polar dual-domain network (DDNet), which for the first time considers the complementary of the Cartesian domain and the polar domain.

Feature Importance

C-MIL: Continuation Multiple Instance Learning for Weakly Supervised Object Detection

1 code implementation CVPR 2019 Fang Wan, Chang Liu, Wei Ke, Xiangyang Ji, Jianbin Jiao, Qixiang Ye

Weakly supervised object detection (WSOD) is a challenging task when provided with image category supervision but required to simultaneously learn object locations and object detectors.

Multiple Instance Learning object-detection +2

Improving Object Detection with Inverted Attention

1 code implementation28 Mar 2019 Zeyi Huang, Wei Ke, Dong Huang

Our approach (1) operates along both the spatial and channels dimensions of the feature maps; (2) requires no extra training on hard samples, no extra network parameters for attention estimation, and no testing overheads.

object-detection Object Detection

Linear Span Network for Object Skeleton Detection

no code implementations ECCV 2018 Chang Liu, Wei Ke, Fei Qin, Qixiang Ye

Hinted by this, we formalize a Linear Span framework, and propose Linear Span Network (LSN) modified by Linear Span Units (LSUs), which minimize the reconstruction error of convolutional network.

Object Skeleton Detection

SRN: Side-output Residual Network for Object Reflection Symmetry Detection and Beyond

1 code implementation17 Jul 2018 Wei Ke, Jie Chen, Jianbin Jiao, Guoying Zhao, Qixiang Ye

The end-to-end deep learning approach, referred to as a side-output residual network (SRN), leverages the output residual units (RUs) to fit the errors between the object ground-truth symmetry and the side-outputs of multiple stages.

Benchmark Edge Detection +2

SRN: Side-output Residual Network for Object Symmetry Detection in the Wild

1 code implementation CVPR 2017 Wei Ke, Jie Chen, Jianbin Jiao, Guoying Zhao, Qixiang Ye

By stacking RUs in a deep-to-shallow manner, SRN exploits the 'flow' of errors among multiple scales to ease the problems of fitting complex outputs with limited layers, suppressing the complex backgrounds, and effectively matching object symmetry of different scales.

Benchmark Symmetry Detection

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