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Works176 from public data
- Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement2,440
A novel method, Zero-Reference Deep Curve Estimation (Zero-DCE), which formulates light enhancement as a task of image-specific curve estimation with a deep network and shows that it generalizes well to diverse lighting conditions.
- An Underwater Image Enhancement Benchmark Dataset and Beyond2,368
This paper constructs an Underwater Image Enhancement Benchmark (UIEB) including 950 real-world underwater images, 890 of which have the corresponding reference images and proposes an underwater image enhancement network (called Water-Net) trained on this benchmark as a baseline, which indicates the generalization of the proposed UIEB for training Convolutional Neural Networks (CNNs).
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- Global Context-Aware Progressive Aggregation Network for Salient Object Detection504
This work proposes a novel network named GCPANet to effectively integrate low-level appearance features, high-level semantic features, and global context features through some progressive context-aware Feature Interweaved Aggregation modules and generate the saliency map in a supervised way.
- Review of Visual Saliency Detection With Comprehensive Information419
Different types of saliency detection algorithms are reviewed, the important issues of the existing methods are summarized, the existent problems and future works are discussed, and the experimental analysis and discussion are conducted to provide a holistic overview of differentSaliency detection methods.
- PUGAN: Physical Model-Guided Underwater Image Enhancement Using GAN With Dual-Discriminators350
This paper proposes a physical model-guided GAN model for UIE, referred to as PUGAN, and designs the Dual-Discriminators for the style-content adversarial constraint, promoting the authenticity and visual aesthetics of the results.
- Dense Attention Fluid Network for Salient Object Detection in Optical Remote Sensing Images350
An end-to-end Dense Attention Fluid Network (DAFNet) for SOD in optical RSIs that significantly outperforms the existing state-of-the-art SOD competitors is proposed and a new and challenging optical RSI dataset is constructed that contains 2,000 images with pixel-wise saliency annotations.
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- Saliency Detection for Stereoscopic Images Based on Depth Confidence Analysis and Multiple Cues Fusion235
A measure to evaluate the reliability of depth map, and use it to reduce the influence of poor depth map on saliency detection, and two saliency maps are integrated into a final saliency map through weighted-sum method according to their importance.
- CIR-Net: Cross-Modality Interaction and Refinement for RGB-D Salient Object Detection229
A convolutional neural network (CNN) model, named CIR-Net, based on the novel cross-modality interaction and refinement is presented, which outperforms the state-of-the-art saliency detectors both qualitatively and quantitatively.
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- A hybrid method for underwater image correction220
Subjective and objective performance evaluations demonstrate that the proposed method significantly improves both color and visibility of degraded underwater images, and is comparable to and even outperforms several state-of-the-art methods.
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- Frequency Perception Network for Camouflaged Object Detection158
This work proposes a novel learnable and separable frequency perception mechanism driven by the semantic hierarchy in the frequency domain that achieves competitive performance in three popular benchmark datasets both qualitatively and quantitatively.
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- RGB-D Salient Object Detection with Cross-Modality Modulation and Selection149
A cross-modality feature modulation module to enhance feature representations by taking the depth features as prior, which models the complementary relations of RGB-D data and an adaptive feature selection module to select saliency-related features and suppress the inferior ones.
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- Cross-modality Discrepant Interaction Network for RGB-D Salient Object Detection142
A novel Cross-modality Discrepant Interaction Network (CDINet) for RGB-D SOD is proposed, which differentially models the dependence of two modalities according to the feature representations of different layers, and outperforms state-of-the-art methods both quantitatively and qualitatively.
- Perceptual hashing for image authentication: A survey139
In this article, the general structure and classifications of image hashing based tamper detection techniques with their properties are exploited and the evaluation datasets and different performance metrics are discussed.
- Does Thermal Really Always Matter for RGB-T Salient Object Detection?136
A global illumination estimation module to predict the global illuminance score of the image, so as to regulate the role played by the two modalities, and proposes a network named TNet to solve the RGB-T SOD task.
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- Towards Fast and Accurate Real-World Depth Super-Resolution: Benchmark Dataset and Baseline126
This work constructs a large-scale dataset named "RGB-D-D", in which the high-frequency component adaptively decomposed from RGB image to guide the depth map SR, and provides a fast depth map super-resolution (FDSR) baseline.
- An Iterative Co-Saliency Framework for RGBD Images123
An iterative RGBD co-saliency framework is proposed, which utilizes the existing single saliency maps as the initialization, and generates the final RGBD Co-Saliency map by using a refinement-cycle model.
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- SRConvNet: A Transformer-Style ConvNet for Lightweight Image Super-Resolution113
This work thoroughly explores the key differences between ConvNet- and transformer-based SR models, thus presenting SRConvNet, a pure transformer-style ConvNet to compete with the best lightweight SISR models in the trade-off between efficiency and accuracy.
- A parallel down-up fusion network for salient object detection in optical remote sensing images108
A novel Parallel Down-up Fusion network (PDF-Net) for SOD in optical RSIs, which takes full advantage of the in-path low- and high-level features and cross-path multi-resolution features to distinguish diversely scaled salient objects and suppress the cluttered backgrounds.
- Global-and-Local Collaborative Learning for Co-Salient Object Detection105
The proposed GLNet is evaluated on three prevailing CoSOD benchmark datasets, demonstrating that the model trained on a small dataset still outperforms 11 state-of-the-art competitors trained on some large datasets.
- HSCS: Hierarchical Sparsity Based Co-saliency Detection for RGBD Images104
A novel co-saliency detection method for RGBD images based on hierarchical sparsity reconstruction and energy function refinement is proposed, which outperforms the state-of-the-art algorithms both qualitatively and quantitatively.
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- Point-aware Interaction and CNN-induced Refinement Network for RGB-D Salient Object Detection99
This work introduces CNNs-assisted Transformer architecture and proposes a novel RGB-D SOD network with Point-aware Interaction and CNN-induced Refinement (PICR-Net), which achieves competitive results in both quantitative and qualitative comparisons.
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- Dense-Localizing Audio-Visual Events in Untrimmed Videos: A Large-Scale Benchmark and Baseline80
The first Untrimmed Audio-Visual (UnAV-J 00) dataset is introduced, which contains 10K untrimmed videos with over 30K audio-visual events, and a new learning-based framework is formulated, which is capable of fully integrating audio and visual modalities to localize audio- visual events with various lengths and capture dependencies between them in a single pass.
- A Weakly Supervised Learning Framework for Salient Object Detection via Hybrid Labels79
A new weakly-supervised SOD task under hybrid labels, where the supervision labels include a large number of coarse labels generated by the traditional unsupervised method and a small number of real labels, and a new pipeline framework with three sophisticated training strategies is designed.
- Joint spatial-spectral hyperspectral image classification based on convolutional neural network72
Experimental results on two datasets demonstrate that the proposed joint spatial-spectral hyperspectral image classification method outperforms other state-of-the-art methods qualitatively and quantitatively.
- Learning Detail-Structure Alternative Optimization for Blind Super-Resolution69
An effective and kernel-free network, namely DSSR, which enables recurrent detail-structure alternative optimization without blur kernel prior incorporation for blind SR and achieves the state-of-the-art against existing methods.
- BridgeNet: A Joint Learning Network of Depth Map Super-Resolution and Monocular Depth Estimation66
This work proposes a joint learning network of depth map super-resolution (DSR) and monocular depth estimation (MDE) without introducing additional supervision labels, which can provide a paradigm for associating the DSR and MDE tasks.
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- Boundary Guided Semantic Learning for Real-Time COVID-19 Lung Infection Segmentation System62
A boundary guided semantic learning network (BSNet) that combines the top-level semantic preservation and progressive semantic integration is designed to model the complementary relationship between different high-level features, thereby promoting the generation of more complete segmentation results.
- PSNet: Parallel Symmetric Network for Video Salient Object Detection52
A VSOD network with up and down parallel symmetry, named PSNet is proposed and the Importance Perception Fusion (IPF) module is used to fuse the features from two parallel branches according to their different importance in different scenarios.
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- Query-Guided Prototype Evolution Network for Few-Shot Segmentation50
The Query-guided Prototype Evolution Network (QPENet) is presented, a new method that integrates query features into the generation process of foreground and background prototypes, thereby yielding customized prototypes attuned to specific queries.
- BCS-Net: Boundary, Context, and Semantic for Automatic COVID-19 Lung Infection Segmentation From CT Images50
A novel network for automatic COVID-19 lung infection segmentation from computed tomography (CT) images, named BCS-Net, which considers the boundary, context, and semantic attributes, and outperforms the existing competitors both qualitatively and quantitatively.
- Single underwater image enhancement based on color cast removal and visibility restoration50
Experimental results demonstrate that the proposed underwater image-enhancement method can effectively remove color cast, improve contrast and visibility, and recover natural appearance of degraded underwater images.
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- Occlusion-aware Bi-directional Guided Network for Light Field Salient Object Detection42
A learning-based network is proposed to exploit occlusion features from EPIs and integrate high-level features from the central view for accurate salient object detection and achieves state-of-the-art performance in both segmentation accuracy and edge clarity.
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- Position-based anchor optimization for point supervised dense nuclei detection40
A novel point-supervised dense nuclei detection framework that introduces position-based anchor optimization to complete morphology-based pseudo-label supervision and an Adaptive Anchor Selector operation is proposed to automatically select positive and negative anchors according to morphological and positional statistical characteristics of nuclei.
- Color-Guided Depth Map Super Resolution Using Convolutional Neural Network39
A dual-stream convolutional neural network, which integrates the color and depth information simultaneously, is proposed for depth map super resolution and experimental results demonstrate the effectiveness of the proposed method compared with the state-of-the-art methods.
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- Learning Common Semantics via Optimal Transport for Contrastive Multi-View Clustering32
This paper presents CSOT, namely Common Semantics via Optimal Transport, to boost contrastive multi-view clustering via semantic learning in a common space that integrates all views and proposes a semantic-aware re-weighting strategy to treat samples differently according to their semantic significance, which improves the effectiveness of cross-view contrastive representation learning.
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- NTIRE 2025 Challenge on Event-Based Image Deblurring: Methods and Results28
This paper presents an overview of NTIRE 2025, the First Challenge on Event-Based Image Deblurring, detailing the proposed methodologies and corresponding results, and anticipate that this challenge will drive further advancements in event-based vision research.
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- SDDNet: Style-guided Dual-layer Disentanglement Network for Shadow Detection26
A Style-guided Dual-layer Disentanglement Network (SDDNet) to model these layers independently, and devise a Feature Separation and Recombination (FSR) module that decomposes multi-level features into shadow-related and background-related components.
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- NuI-Go: Recursive Non-Local Encoder-Decoder Network for Retinal Image Non-Uniform Illumination Removal20
A non-uniform illumination removal network for retinal image, called NuI-Go, which consists of three Recursive Non-local Encoder-Decoder Residual Blocks (NEDRBs) for enhancing the degraded retinal images in a progressive manner is proposed.
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- Learning Hierarchical Color Guidance for Depth Map Super-Resolution16
This article rethink the utilization of color information and proposes a hierarchical color guidance network (HCGNet) to achieve DSR, which achieves more competitive performance both qualitatively and quantitatively.
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- Stereo-GS: Multi-View Stereo Vision Model for Generalizable 3D Gaussian Splatting Reconstruction11
Stereo-GS, a disentangled framework for efficient 3D Gaussian prediction, extracts features from local image pairs using a stereo vision backbone and fuses them via global attention blocks to achieve pose-free 3D reconstruction, improving robustness and practicality.
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- Unpaired remote sensing image super-resolution with content-preserving weak supervision neural network9
This paper presents a meta-modelling framework that automates the very labor-intensive and therefore time-heavy and expensive and therefore expensive and expensive process of computer programming called “supercomputing”.
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- GGRNet: Global Graph Reasoning Network for Salient Object Detection in Optical Remote Sensing Images7
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- Manmade target extraction based on multistage decision and its application for change detection in polarimetric synthetic aperture radar image3
A universal framework of change detection for manmade targets is presented as an application of the proposed multistage decision-based method based on the theories of polarimetric contrast enhancement, generalized Y decomposition, and maximum eigenvalue ratio.
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- TDS-CLIP: Temporal Difference Side Network for Efficient Video Action Recognition2
A novel memory-efficient Temporal Difference Side Network (TDS-CLIP) is proposed to balance knowledge transferring and temporal modeling, avoiding backpropagation in frozen parameter models and introduces a Temporal Difference Adapter (TD-Adapter), which can effectively capture local temporal differences in motion features to strengthen the model's global temporal modeling capabilities.
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- OVFormer+: Improved Open-Vocabulary Video Instance Segmentation via Text-Guided Unified Embedding Alignment–
This work designs and trains a novel OV-VIS baseline called OVFormer+.
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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 2026-10-13. 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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