348 citations · 809 across the 22 of their papers we have counts for
11 papers · 1 filter
Multi-Projection Fusion and Refinement Network for Salient Object Detection in 360° Omnidirectional Image
Runmin Cong, Ke Huang, Jianjun Lei +3
Salient object detection (SOD) aims to determine the most visually attractive objects in an image. With the development of virtual reality technology, 360° omnidirectional image ha…
Mask Matching Transformer for Few-Shot Segmentation
Siyu Jiao, Gengwei Zhang, Shant Navasardyan +4
In this paper, we aim to tackle the challenging few-shot segmentation task from a new perspective. Typical methods follow the paradigm to firstly learn prototypical features from s…
Bridging Component Learning with Degradation Modelling for Blind Image Super-Resolution
Yixuan Wu, Feng Li, Huihui Bai +3
Convolutional Neural Network (CNN)-based image super-resolution (SR) has exhibited impressive success on known degraded low-resolution (LR) images. However, this type of approach i…
Learning Detail-Structure Alternative Optimization for Blind Super-Resolution
Feng Li, Yixuan Wu, Huihui Bai +3
Existing convolutional neural networks (CNN) based image super-resolution (SR) methods have achieved impressive performance on bicubic kernel, which is not valid to handle unknown…
PSNet: Parallel Symmetric Network for Video Salient Object Detection
Runmin Cong, Weiyu Song, Jianjun Lei +3
For the video salient object detection (VSOD) task, how to excavate the information from the appearance modality and the motion modality has always been a topic of great concern. T…
Does Thermal Really Always Matter for RGB-T Salient Object Detection?
Runmin Cong, Kepu Zhang, Chen Zhang +4
In recent years, RGB-T salient object detection (SOD) has attracted continuous attention, which makes it possible to identify salient objects in environments such as low light by i…
CIR-Net: Cross-modality Interaction and Refinement for RGB-D Salient Object Detection
Runmin Cong, Qinwei Lin, Chen Zhang +4
Focusing on the issue of how to effectively capture and utilize cross-modality information in RGB-D salient object detection (SOD) task, we present a convolutional neural network (…
A Weakly Supervised Learning Framework for Salient Object Detection via Hybrid Labels
Runmin Cong, Qi Qin, Chen Zhang +4
Fully-supervised salient object detection (SOD) methods have made great progress, but such methods often rely on a large number of pixel-level annotations, which are time-consuming…
Boundary Guided Semantic Learning for Real-time COVID-19 Lung Infection Segmentation System
Runmin Cong, Yumo Zhang, Ning Yang +6
The coronavirus disease 2019 (COVID-19) continues to have a negative impact on healthcare systems around the world, though the vaccines have been developed and national vaccination…
BCS-Net: Boundary, Context and Semantic for Automatic COVID-19 Lung Infection Segmentation from CT Images
Runmin Cong, Haowei Yang, Qiuping Jiang +5
The spread of COVID-19 has brought a huge disaster to the world, and the automatic segmentation of infection regions can help doctors to make diagnosis quickly and reduce workload.…
Global-and-Local Collaborative Learning for Co-Salient Object Detection
Runmin Cong, Ning Yang, Chongyi Li +4
The goal of co-salient object detection (CoSOD) is to discover salient objects that commonly appear in a query group containing two or more relevant images. Therefore, how to effec…