2 citations · 6 across the 6 of their papers we have counts for
6 papers
A Task-aware Dual Similarity Network for Fine-grained Few-shot Learning
Yan Qi, Han Sun, Ningzhong Liu +1
The goal of fine-grained few-shot learning is to recognize sub-categories under the same super-category by learning few labeled samples. Most of the recent approaches adopt a singl…
Polycentric Clustering and Structural Regularization for Source-free Unsupervised Domain Adaptation
Xinyu Guan, Han Sun, Ningzhong Liu +1
Source-Free Domain Adaptation (SFDA) aims to solve the domain adaptation problem by transferring the knowledge learned from a pre-trained source model to an unseen target domain. M…
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…
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…
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…