348 citations · 809 across the 22 of their papers we have counts for
24 papers
Decoupling Spherical Reasoning from Dense Prediction for 360 Depth Estimation
Zhijie Shen, Chunyu Lin, Shuai Zheng +4
The equirectangular projection (ERP) is widely used for panoramic depth estimation, but its spatially varying distortion makes geometry-consistent feature modeling challenging. We…
SphereSOD: Geometry-Structure Coupled Learning for 360 Salient Object Detection
Junsong Zhang, Zhijie Shen, Shuai Zheng +4
360° salient object detection (SOD) aims to accurately segment salient regions across a full field of view. However, equirectangular projection (ERP) introduces severe spatial dist…
SDDNet: Style-guided Dual-layer Disentanglement Network for Shadow Detection
Runmin Cong, Yuchen Guan, Jinpeng Chen +3
Despite significant progress in shadow detection, current methods still struggle with the adverse impact of background color, which may lead to errors when shadows are present on c…
You Can Mask More For Extremely Low-Bitrate Image Compression
Anqi Li, Feng Li, Jiaxin Han +6
Learned image compression (LIC) methods have experienced significant progress during recent years. However, these methods are primarily dedicated to optimizing the rate-distortion…
Exploring Resolution Fields for Scalable Image Compression with Uncertainty Guidance
Dongyi Zhang, Feng Li, Man Liu +4
Recently, there are significant advancements in learning-based image compression methods surpassing traditional coding standards. Most of them prioritize achieving the best rate-di…
Progressive Semantic-Visual Mutual Adaption for Generalized Zero-Shot Learning
Man Liu, Feng Li, Chunjie Zhang +3
Generalized Zero-Shot Learning (GZSL) identifies unseen categories by knowledge transferred from the seen domain, relying on the intrinsic interactions between visual and semantic…
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…
RRNet: Relational Reasoning Network with Parallel Multi-scale Attention for Salient Object Detection in Optical Remote Sensing Images
Runmin Cong, Yumo Zhang, Leyuan Fang +3
Salient object detection (SOD) for optical remote sensing images (RSIs) aims at locating and extracting visually distinctive objects/regions from the optical RSIs. Despite some sal…
Cross-modality Discrepant Interaction Network for RGB-D Salient Object Detection
Chen Zhang, Runmin Cong, Qinwei Lin +4
The popularity and promotion of depth maps have brought new vigor and vitality into salient object detection (SOD), and a mass of RGB-D SOD algorithms have been proposed, mainly co…
BridgeNet: A Joint Learning Network of Depth Map Super-Resolution and Monocular Depth Estimation
Qi Tang, Runmin Cong, Ronghui Sheng +4
Depth map super-resolution is a task with high practical application requirements in the industry. Existing color-guided depth map super-resolution methods usually necessitate an e…
Dense Attention Fluid Network for Salient Object Detection in Optical Remote Sensing Images
Qijian Zhang, Runmin Cong, Chongyi Li +5
Despite the remarkable advances in visual saliency analysis for natural scene images (NSIs), salient object detection (SOD) for optical remote sensing images (RSIs) still remains a…
CoADNet: Collaborative Aggregation-and-Distribution Networks for Co-Salient Object Detection
Qijian Zhang, Runmin Cong, Junhui Hou +2
Co-Salient Object Detection (CoSOD) aims at discovering salient objects that repeatedly appear in a given query group containing two or more relevant images. One challenging issue…
Learning Deep Interleaved Networks with Asymmetric Co-Attention for Image Restoration
Feng Li, Runmin Cong, Huihui Bai +3
Recently, convolutional neural network (CNN) has demonstrated significant success for image restoration (IR) tasks (e.g., image super-resolution, image deblurring, rain streak remo…
A Parallel Down-Up Fusion Network for Salient Object Detection in Optical Remote Sensing Images
Chongyi Li, Runmin Cong, Chunle Guo +4
The diverse spatial resolutions, various object types, scales and orientations, and cluttered backgrounds in optical remote sensing images (RSIs) challenge the current salient obje…