Search Results for author: Wei Ke

Found 17 papers, 8 papers with code

Community Detection Using Revised Medoid-Shift Based on KNN

no code implementations19 Apr 2023 Jie Hou, Jiakang Li, Xiaokang Peng, Wei Ke, Yonggang Lu

During the process of finding the next medoid, the RMS algorithm is based on a neighborhood defined by KNN, while the original Medoid-Shift is based on a neighborhood defined by a distance parameter.

Clustering Community Detection

Crowd Counting with Sparse Annotation

no code implementations12 Apr 2023 Shiwei Zhang, Zhengzheng Wang, Qing Liu, Fei Wang, Wei Ke, Tong Zhang

This paper presents a new annotation method called Sparse Annotation (SA) for crowd counting, which reduces human labeling efforts by sparsely labeling individuals in an image.

Crowd Counting

De-coupling and De-positioning Dense Self-supervised Learning

no code implementations29 Mar 2023 Congpei Qiu, Tong Zhang, Wei Ke, Mathieu Salzmann, Sabine Süsstrunk

Dense Self-Supervised Learning (SSL) methods address the limitations of using image-level feature representations when handling images with multiple objects.

Data Augmentation object-detection +3

CoupAlign: Coupling Word-Pixel with Sentence-Mask Alignments for Referring Image Segmentation

no code implementations4 Dec 2022 ZiCheng Zhang, Yi Zhu, Jianzhuang Liu, Xiaodan Liang, Wei Ke

Then in the Sentence-Mask Alignment (SMA) module, the masks are weighted by the sentence embedding to localize the referred object, and finally projected back to aggregate the pixels for the target.

Image Segmentation Semantic Segmentation +2

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.

Edge Detection Hand Pose Estimation +1

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.

Symmetry Detection

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