150 citations · 450 across the 43 of their papers we have counts for
45 papers
MARLINE: Multi-Source Mapping Transfer Learning for Non-Stationary Environments
Honghui Du, Leandro Minku, Huiyu Zhou
Concept drift is a major problem in online learning due to its impact on the predictive performance of data stream mining systems. Recent studies have started exploring data stream…
DiffUCD:Unsupervised Hyperspectral Image Change Detection with Semantic Correlation Diffusion Model
Xiangrong Zhang, Shunli Tian, Guanchun Wang +2
Hyperspectral image change detection (HSI-CD) has emerged as a crucial research area in remote sensing due to its ability to detect subtle changes on the earth's surface. Recently,…
SRCNet: Seminal Representation Collaborative Network for Marine Oil Spill Segmentation
Fang Chen, Heiko Balzter, Peng Ren +1
Effective oil spill segmentation in Synthetic Aperture Radar (SAR) images is critical for marine oil pollution cleanup, and proper image representation is helpful for accurate imag…
DGNet: Distribution Guided Efficient Learning for Oil Spill Image Segmentation
Fang Chen, Heiko Balzter, Feixiang Zhou +2
Successful implementation of oil spill segmentation in Synthetic Aperture Radar (SAR) images is vital for marine environmental protection. In this paper, we develop an effective se…
Deep Learning Methods for Calibrated Photometric Stereo and Beyond
Yakun Ju, Kin-Man Lam, Wuyuan Xie +3
Photometric stereo recovers the surface normals of an object from multiple images with varying shading cues, i.e., modeling the relationship between surface orientation and intensi…
DU-Net based Unsupervised Contrastive Learning for Cancer Segmentation in Histology Images
Yilong Li, Yaqi Wang, Huiyu Zhou +3
In this paper, we introduce an unsupervised cancer segmentation framework for histology images. The framework involves an effective contrastive learning scheme for extracting disti…
A lightweight multi-scale context network for salient object detection in optical remote sensing images
Yuhan Lin, Han Sun, Ningzhong Liu +3
Due to the more dramatic multi-scale variations and more complicated foregrounds and backgrounds in optical remote sensing images (RSIs), the salient object detection (SOD) for opt…
Salient Skin Lesion Segmentation via Dilated Scale-Wise Feature Fusion Network
Pourya Shamsolmoali, Masoumeh Zareapoor, Eric Granger +1
Skin lesion detection in dermoscopic images is essential in the accurate and early diagnosis of skin cancer by a computerized apparatus. Current skin lesion segmentation approaches…
Absolute Wrong Makes Better: Boosting Weakly Supervised Object Detection via Negative Deterministic Information
Guanchun Wang, Xiangrong Zhang, Zelin Peng +3
Weakly supervised object detection (WSOD) is a challenging task, in which image-level labels (e.g., categories of the instances in the whole image) are used to train an object dete…
Neuroplastic graph attention networks for nuclei segmentation in histopathology images
Yoav Alon, Huiyu Zhou
Modern histopathological image analysis relies on the segmentation of cell structures to derive quantitative metrics required in biomedical research and clinical diagnostics. State…
Oil Spill SAR Image Segmentation via Probability Distribution Modelling
Fang Chen, Aihua Zhang, Heiko Balzter +2
Segmentation of marine oil spills in Synthetic Aperture Radar (SAR) images is a challenging task because of the complexity and irregularities in SAR images. In this work, we aim to…
Anchor Retouching via Model Interaction for Robust Object Detection in Aerial Images
Dong Liang, Qixiang Geng, Zongqi Wei +4
Object detection has made tremendous strides in computer vision. Small object detection with appearance degradation is a prominent challenge, especially for aerial observations. To…
Semantically Contrastive Learning for Low-light Image Enhancement
Dong Liang, Ling Li, Mingqiang Wei +5
Low-light image enhancement (LLE) remains challenging due to the unfavorable prevailing low-contrast and weak-visibility problems of single RGB images. In this paper, we respond to…
Dispensed Transformer Network for Unsupervised Domain Adaptation
Yunxiang Li, Jingxiong Li, Ruilong Dan +10
Accurate segmentation is a crucial step in medical image analysis and applying supervised machine learning to segment the organs or lesions has been substantiated effective. Howeve…
Structure-aware scale-adaptive networks for cancer segmentation in whole-slide images
Yibao Sun, Giussepi Lopez, Yaqi Wang +3
Cancer segmentation in whole-slide images is a fundamental step for viable tumour burden estimation, which is of great value for cancer assessment. However, factors like vague boun…
Robust Ensembling Network for Unsupervised Domain Adaptation
Han Sun, Lei Lin, Ningzhong Liu +1
Recently, in order to address the unsupervised domain adaptation (UDA) problem, extensive studies have been proposed to achieve transferrable models. Among them, the most prevalent…
Multi-scale Edge-based U-shape Network for Salient Object Detection
Han Sun, Yetong Bian, Ningzhong Liu +1
Deep-learning based salient object detection methods achieve great improvements. However, there are still problems existing in the predictions, such as blurry boundary and inaccura…
Multi-patch Feature Pyramid Network for Weakly Supervised Object Detection in Optical Remote Sensing Images
Pourya Shamsolmoali, Jocelyn Chanussot, Masoumeh Zareapoor +2
Object detection is a challenging task in remote sensing because objects only occupy a few pixels in the images, and the models are required to simultaneously learn object location…
MPI: Multi-receptive and Parallel Integration for Salient Object Detection
Han Sun, Jun Cen, Ningzhong Liu +2
The semantic representation of deep features is essential for image context understanding, and effective fusion of features with different semantic representations can significantl…
Adaptive Affinity Loss and Erroneous Pseudo-Label Refinement for Weakly Supervised Semantic Segmentation
Xiangrong Zhang, Zelin Peng, Peng Zhu +4
Semantic segmentation has been continuously investigated in the last ten years, and majority of the established technologies are based on supervised models. In recent years, image-…
Semantic Attention and Scale Complementary Network for Instance Segmentation in Remote Sensing Images
Tianyang Zhang, Xiangrong Zhang, Peng Zhu +4
In this paper, we focus on the challenging multicategory instance segmentation problem in remote sensing images (RSIs), which aims at predicting the categories of all instances and…
Incorporating Lambertian Priors into Surface Normals Measurement
Yakun Ju, Muwei Jian, Shaoxiang Guo +3
The goal of photometric stereo is to measure the precise surface normal of a 3D object from observations with various shading cues. However, non-Lambertian surfaces influence the m…
Magnification-independent Histopathological Image Classification with Similarity-based Multi-scale Embeddings
Yibao Sun, Xingru Huang, Yaqi Wang +2
The classification of histopathological images is of great value in both cancer diagnosis and pathological studies. However, multiple reasons, such as variations caused by magnific…
SRPN: similarity-based region proposal networks for nuclei and cells detection in histology images
Yibao Sun, Xingru Huang, Huiyu Zhou +1
The detection of nuclei and cells in histology images is of great value in both clinical practice and pathological studies. However, multiple reasons such as morphological variatio…