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Works122 from public data
- Dense Nested Attention Network for Infrared Small Target Detection1,048
This paper designs a dense nested interactive module (DNIM) to achieve progressive interaction among high-level and low-level features and proposes a cascaded channel and spatial attention module (CSAM) to adaptively enhance multi- level features.
- Unsupervised Degradation Representation Learning for Blind Super-Resolution419
This paper proposes an unsupervised degradation representation learning scheme for blind SR without explicit degradation estimation, and introduces a Degradation-Aware SR (DASR) network with flexible adaption to various degradations based on the learned representations.
- 320
- Learning Parallax Attention for Stereo Image Super-Resolution320
A parallax-attention mechanism with a global receptive field along the epipolar line to handle different stereo images with large disparity variations is introduced and a new and the largest dataset for stereo image SR is proposed.
- Spatial-Angular Interaction for Light Field Image Super-Resolution208
Experimental results demonstrate the superiority of LF-InterNet over the state-of-the-art methods, i.e., the method can achieve high PSNR and SSIM scores with low computational cost, and recover faithful details in the reconstructed images.
- Light Field Image Super-Resolution With Transformers190
This paper proposes a simple but effective Transformer-based method for LF image SR, designed to incorporate complementary information among different views, and achieves superior SR performance with a small model size and low computational cost.
- Deep Video Super-Resolution Using HR Optical Flow Estimation176
This paper proposes an end-to-end video SR network to super-resolve both optical flows and images and shows that the network achieves the state-of-the-art performance.
- Deformable 3D Convolution for Video Super-Resolution170
A deformable 3D convolution network (D3Dnet) is proposed to incorporate spatio-temporal information from both spatial and temporal dimensions for video SR, and achieves state-of-the-art SR performance.
- Learning A Single Network for Scale-Arbitrary Super-Resolution162
This paper develops a plug-in module for existing SR networks to perform scale-arbitrary SR, which consists of multiple scale-aware feature adaption blocks and a scale- aware upsampling layer that enables the network to adapt to arbitrary scale factors.
- Parallax Attention for Unsupervised Stereo Correspondence Learning159
A generic parallax-attention mechanism (PAM) to capture stereo correspondence regardless of disparity variations is proposed and Experimental results show that the PAM is generic and can effectively learn stereo correspondence under large disparity variations in an unsupervised manner.
- 153
- A Stereo Attention Module for Stereo Image Super-Resolution143
Using SAM, the generic stereo attention module (SAM) is proposed to extend arbitrary SISR networks to stereo images and can exploit cross-view information while maintaining the superiority of intra-View information exploitation, resulting in notable performance gain to SisR networks.
- Occlusion-Aware Cost Constructor for Light Field Depth Estimation114
A simple and fast cost constructor to construct matching cost for LF depth estimation, composed by a series of convolutions with specifically designed dilation rates, which is occlusion-aware and can handle occlusions by dynamically modulating pixels from different views.
- Symmetric Parallax Attention for Stereo Image Super-Resolution112
This paper proposes a symmetric bi-directional parallax attention module (biPAM) and an inline occlusion handling scheme to effectively interact cross-view information and designs a Siamese network equipped with a biPAM to super-resolve both sides of views in a highly symmetric manner.
- 107
- Learning for Video Super-Resolution Through HR Optical Flow Estimation107
This paper proposes an end-to-end trainable video SR framework to super-resolve both images and optical flows and demonstrates that HR optical flows provide more accurate correspondences than their LR counterparts and improve both accuracy and consistency performance.
- Learning Non-Local Spatial-Angular Correlation for Light Field Image Super-Resolution102
This paper adopts the epipolar plane image (EPI) representation, develops a Transformer network with repetitive self-attention operations to learn the spatial-angular correlation, and develops a Transformer network with repetitive self-attention operations to learn the spatial-angular correlation.
- DeOccNet: Learning to See Through Foreground Occlusions in Light Fields94
This paper handles the LF de-occlusion (LF-DeOcc) problem using a deep encoder-decoder network (namely, DeOccNet), and is the first deep learning-based LF-De Occ method.
- 80
- A Systematic Survey of Deep Learning-Based Single-Image Super-Resolution79
This survey gives an overview of DL-based SISR methods and group them according to their design targets and presents the reconstruction results of some classic SISR methods to intuitively know their performance.
- Decoupling Makes Weakly Supervised Local Feature Better74
This paper proposes a decoupled training describe-then-detect pipeline tailored for weakly supervised local feature learning, and introduces a line-to-window search strategy to explicitly use the camera pose information for better descriptor learning.
- Motion and Appearance Decoupling Representation for Event Cameras61
Inspired by human visual prior, a motion and appearance decoupling (MAD) event representation is proposed to disentangle the mixed spatial-temporal event tensor into two independent branches to reduce the network’s learning burden toward complex high-level interpretation tasks.
- Monte Carlo Linear Clustering with Single-Point Supervision is Enough for Infrared Small Target Detection60
This work proposes the first method to achieve SIRST detection with single-point supervision, and can accurately recover pseudo masks and thus turn arbitrary fully supervised SIRST detection networks into weakly supervised ones with only single point annotation.
- Local Motion and Contrast Priors Driven Deep Network for Infrared Small Target Superresolution59
This article proposes the first infrared small target SR method named local motion and contrast prior driven deep network (MoCoPnet) to integrate the domain knowledge of therared small target into deep network, which can mitigate the intrinsic feature scarcity of infrared small targets.
- Lightweight Pixel Difference Networks for Efficient Visual Representation Learning58
Two novel types of convolutions are proposed, dubbed Pixel Difference Convolution (PDC) and Binary PDC (Bi-PDC), which enjoy the following benefits: capturing higher-order local differential information, computationally efficient, and able to be integrated with existing DNNs.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-14. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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