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Works388 from public data
- FusionGAN: A generative adversarial network for infrared and visible image fusion2,265
This paper proposes a novel method to fuse two types of information using a generative adversarial network, termed as FusionGAN, which establishes an adversarial game between a generator and a discriminator, where the generator aims to generate a fused image with major infrared intensities together with additional visible gradients.
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- Image Matching from Handcrafted to Deep Features: A Survey988
This survey introduces feature detection, description, and matching techniques from handcrafted methods to trainable ones and provides an analysis of the development of these methods in theory and practice, and briefly introduces several typical image matching-based applications.
- Multi-Scale Progressive Fusion Network for Single Image Deraining859
This work explores the multi-scale collaborative representation for rain streaks from the perspective of input image scales and hierarchical deep features in a unified framework, termed multi- scale progressive fusion network (MSPFN) for single image rain streak removal.
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- FusionDN: A Unified Densely Connected Network for Image Fusion658
A new unsupervised and unified densely connected network for different types of image fusion tasks, termed as FusionDN, which obtains a single model applicable to multiple fusion tasks by applying elastic weight consolidation to avoid forgetting what has been learned from previous tasks when training multiple tasks sequentially.
- Locality Preserving Matching601
The authors' method can accomplish the mismatch removal from thousands of putative correspondences in only a few milliseconds, and achieves better or favorably competitive performance in accuracy while intensively cutting time cost by more than two orders of magnitude.
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- Pan-GAN: An unsupervised pan-sharpening method for remote sensing image fusion471
This work proposes a novel unsupervised framework for pan-sharpening based on a generative adversarial network, termed as Pan-GAN, which does not rely on the so-called ground-truth during network training and has shown promising performance in terms of qualitative visual effects and quantitative evaluation metrics.
- Infrared and visible image fusion via detail preserving adversarial learning456
This paper proposes an end-to-end model for infrared and visible image fusion based on detail preserving adversarial learning that is able to overcome the limitations of the manual and complicated design of activity-level measurement and fusion rules in traditional fusion methods.
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- BinsFormer: Revisiting Adaptive Bins for Monocular Depth Estimation318
A novel framework called BinsFormer, tailored for the classification-regression-based depth estimation, which mainly focuses on two crucial components in the specific task: proper generation of adaptive bins and sufficient interaction between probability distribution and bins predictions.
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- DepthFormer: Exploiting Long-range Correlation and Local Information for Accurate Monocular Depth Estimation270
A hierarchical aggregation and heterogeneous interaction module to enhance the Transformer features and model the affinity between the heterogeneous features in a set-to-set translation manner and surpasses state-of-the-art monocular depth estimation methods with prominent margins.
- Hierarchical dense recursive network for image super-resolution242
A novel hierarchical dense connection network (HDN) is advocated for image SR that outperforms the state-of-the-art methods in terms of quantitative indicators and realistic visual effects, as well as enjoys a fast and accurate reconstruction.
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- Shadows can be Dangerous: Stealthy and Effective Physical-world Adversarial Attack by Natural Phenomenon214
A new type of optical adversarial examples, in which the perturbations are generated by a very common natural phenomenon, shadow, to achieve naturalistic and stealthy physical-world adversarial attack under the black-box setting is studied.
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- Hyperspectral Image Classification in the Presence of Noisy Labels203
Experimental studies show that the RLPA can reduce the level of noisy label and demonstrates the advantages of the proposed method over four major classifiers with a significant margin—the gains in terms of the average overall accuracy, average accuracy, and kappa are impressive.
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- Deep Learning-based Face Super-resolution: A Survey166
This survey presents a comprehensive review of deep learning-based FSR methods in a systematic manner and roughly categorizes existing methods according to the utilization of facial characteristics.
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- Deep Distillation Recursive Network for Remote Sensing Imagery Super-Resolution144
Experiments on Kaggle Open Source Dataset and Jilin-1 video satellite images illustrate that DDRN outperforms the conventional CNN-based baselines and some state-of-the-art feature extraction approaches.
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- Feature guided Gaussian mixture model with semi-supervised EM and local geometric constraint for retinal image registration137
A feature guided Gaussian mixture model (GMM) is proposed for the non-rigid registration of retinal images that is robust in different registration tasks and outperforms several competing approaches, especially when data is severely degraded.
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- Satellite Image Super-Resolution via Multi-Scale Residual Deep Neural Network127
Experimental results on the SpaceNet satellite image and NWPU-RESISC45 databases show that the proposed approach outperformed several state-of-the-art SR algorithms in terms of objective and subjective image qualities.
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- Learning a Generative Model for Fusing Infrared and Visible Images via Conditional Generative Adversarial Network with Dual Discriminators120
By constraining and distinguishing between the downsampled fused image and the lowresolution infrared image, DDcGAN can be preferably applied to the fusion of different resolution images.
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- A Survey on All-in-One Image Restoration: Taxonomy, Evaluation and Future Trends107
This survey provides the first in-depth and systematic overview of AiOIR, delivering a structured taxonomy that categorizes existing methods by architectural designs, learning paradigms, and their core innovations, and systematically categorizes current approaches and assess the challenges these models encounter.
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- Face Hallucination via Split-Attention in Split-Attention Network98
Experimental results on face hallucination and face recognition unveil that the proposed method not only significantly improves the clarity of hallucinated faces, but also encourages the subsequent face recognition performance substantially.
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- Spectral–Spatial and Superpixelwise PCA for Unsupervised Feature Extraction of Hyperspectral Imagery96
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- Omniscient Video Super-Resolution92
This paper proposes an omniscient framework to not only utilize the preceding SR output, but also leverage the SR outputs from the present and future to enable a generator to behave better than its counterparts under other frameworks.
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- A Practical Contrastive Learning Framework for Single-Image Super-Resolution84
This article proposes a practical contrastive learning framework for SISR (PCL-SR) that involves the generation of many informative positive and hard negative samples in frequency space and designs a simple but effective embedding network inherited from the discriminator network, which is more task-friendly.
- SimIPU: Simple 2D Image and 3D Point Cloud Unsupervised Pre-training for Spatial-Aware Visual Representations84
This is the first study to explore contrastive learning pre-training strategies for outdoor multi-modal datasets, containing paired camera images and LIDAR point clouds, in an unsupervised end-to-end fashion.
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- MaeFuse: Transferring Omni Features With Pretrained Masked Autoencoders for Infrared and Visible Image Fusion via Guided Training83
MaeFuse not only introduces a novel perspective in the realm of fusion techniques but also stands out with impressive performance across various public datasets, which can facilitate the comprehensive integration of feature vectors from both infrared and visible modalities.
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- Learning with Noisy Labels via Sparse Regularization82
The sparse regularization strategy to approximate the one-hot constraint is introduced, which is composed of network output sharpening operation that enforces the output distribution of a net-work to be sharp and the ℓp-norm regularization that promotes the network output to be sparse.
- Learning Sparse and Identity-Preserved Hidden Attributes for Person Re-Identification81
This work proposes to incrementally generate Deep Hidden Attribute (DHA) based on baseline deep network for newly uncovered annotations based on an auto-encoder model that can be plugged into any deep network to mine latent information in an unsupervised manner.
- High-Resolution Depth Maps Imaging via Attention-Based Hierarchical Multi-Modal Fusion79
To effectively extract and combine relevant information from LR depth and HR guidance, a multi-modal attention based fusion (MMAF) strategy for hierarchical convolutional layers, including a feature enhancement block to select valuable features and a feature recalibration block to unify the similarity metrics of modalities with different appearance characteristics are proposed.
- Towards End-to-End Image Compression and Analysis with Transformers75
An end-to-end image compression and analysis model with Transformers, targeting to the cloud-based image classification application, which aims to perform image classification from the compressed features and facilitate image compression with the long-term information from the Transformer.
- 3D action recognition using multi-temporal depth motion maps and fisher vector75
Extensive experiments on the public MSRAction3D, MSRGesture3D and DHA datasets show that the proposed method outperforms state-of-the-art approaches for depth-based action recognition.
- Noise robust position-patch based face super-resolution via Tikhonov regularized neighbor representation74
A novel position-patch face super-resolution method based on Tikhonov regularized neighbor representation (TRNR) is developed, which achieves better performance in terms of reconstruction error and visual quality than existing state-of-the-art methods.
- CDMMA: Coupled discriminant multi-manifold analysis for matching low-resolution face images73
A novel method to address the problem of matching a LR or poor quality face image to a gallery of high-resolution (HR) face images and demonstrates the superiority of the proposed coupled discriminant multi-manifold analysis (CDMMA).
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- Self-Supervised Arbitrary-Scale Point Clouds Upsampling via Implicit Neural Representation72
This paper proposes a novel approach that achieves self-supervised and magnification-flexible point clouds upsampling simultaneously, and defines two implicit neural functions to estimate projection direction and distance respectively, which can be trained by two pretext learning tasks.
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- Guided Depth Map Super-Resolution: A Survey71
A comprehensive survey of recent progress in guided depth map super-resolution (GDSR), which aims to reconstruct a high-resolution depth map from a low-resolution observation with the help of a paired high- resolution color image, is presented.
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- Non-Rigid Point Set Registration with Robust Transformation Estimation under Manifold Regularization71
The method iteratively recovers the point correspondence and estimates the spatial transformation between two point sets and can yield superior results compared to other state-of-the-arts, especially in case of badly degraded data.
- Feature-guided Gaussian mixture model for image matching69
Extensive experiments on different real images show that the proposedFG-GMM consistently outperforms other state-of-the-art methods, which validates its robustness.
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- Transforming Image Super-Resolution: A ConvFormer-Based Efficient Approach63
The Convolutional Transformer layer (ConvFormer) is introduced and a ConvFormer-based Super-Resolution network (CFSR) is proposed, offering an effective and efficient solution for lightweight image super-resolution.
- Deep Attentional Guided Image Filtering63
This work proposes an attentional kernel learning module to generate dual sets of filter kernels from the guidance and the target and then adaptively combine them by modeling the pixelwise dependency between the two images.
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- Context-Patch Face Hallucination Based on Thresholding Locality-Constrained Representation and Reproducing Learning62
This paper incorporates the contextual information of the image patch and proposes a powerful and efficient context-patch-based face hallucination approach, namely, thresholding locality-constrained representation and reproducing learning (TLcR-RL).
- DAWN: Direction-aware Attention Wavelet Network for Image Deraining61
A novel Direction-aware Attention Wavelet Network (DAWN) for rain streaks removal that delivers significant performance gains on nine datasets across image deraining and object detection tasks, exceeding the state-of-the-art method MPRNet by 0.88 dB in PSNR on the Test1200 dataset.
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- Multi-scale spectral feature extraction for underwater acoustic target recognition58
The proposed multi-scale spectral features exhibit “fingerprint” characters of underwater acoustic targets, and are robust in highly noisy and constantly changing environment, thus present great advantages in underwater acoustic target recognition.
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- Deep CNN Denoiser and Multi-layer Neighbor Component Embedding for Face Hallucination56
This paper proposes a general face hallucination method that can integrate model-based optimization and discriminative inference, and develops a high-frequency details compensation method by dividing the face image to facial components and performing face hallucinations in a multi-layer neighbor embedding manner.
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- Fully 1 × 1 Convolutional Network for Lightweight Image Super-resolution52
A simple yet effective fully 1 × 1 convolutional network, named shift-Conv-based network (SCNet), equipped with a powerful representation capability and impressive computational efficiency, which can be exploited for lightweight SISR tasks.
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- Spatial-Temporal Space Hand-in-Hand: Spatial-Temporal Video Super-Resolution via Cycle-Projected Mutual Learning46
A one-stage based Cycle-projected Mutual learning network (CycMu-Net) for ST-VSR is proposed, which makes full use of spatial-temporal correlations via the mutual learning between S-V SR and T-vSR, to exploit the mutual information among them via iterative up-and-down projections.
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- Action recognition from depth sequences using weighted fusion of 2D and 3D auto-correlation of gradients features45
An effective depth feature representation is developed based on the fusion of 2D and 3D auto-correlation of gradients features based on a weighted fusion strategy to assign different weights to the classifier probability outputs associated with different features, thereby providing more flexibility in the final decision making.
- Noise robust face hallucination algorithm using local content prior based error shrunk nearest neighbors representation43
A novel error shrunk nearest neighbors representation (ESNNR) based face hallucination algorithm is proposed in this paper, where local content prior is incorporated to identify the high variance content (HVC) in the input images.
- Towards fairness-aware and privacy-preserving enhanced collaborative learning for healthcare42
A framework for healthcare institutions with varying resources to fairly collaborate on AI training while protecting patient privacy is developed, ensuring equitable access to high-quality medical AI regardless of resource constraints.
- Interpretable features for underwater acoustic target recognition42
The experimental results show that these features indeed exist validity for underwater acoustic target recognition with explicit physical interpretability, and lead to very low computational complexity in recognition.
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- Sparse Support Regression for Image Super-Resolution41
A novel single image super-resolution method to simultaneously improve the resolution and perceptual image quality and to preserve the geometrical structure of image patch dictionary, which is critical for reducing artifacts and obtaining better visual quality.
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- Fast and Accurate Single-Image Depth Estimation on Mobile Devices, Mobile AI 2021 Challenge: Report39
This paper introduces the first Mobile AI challenge, where the target is to develop an end-to-end deep learning-based depth estimation solutions that can demonstrate a nearly realtime performance on smartphones and IoT platforms.
- Global-local fusion network for face super-resolution39
A novel global-local fused network (GLFSR) is designed to refine HF information for recovering fine details of facial images and demonstrates that GLFSR is superior to other state-of-the-art deep learning approaches.
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- NTIRE 2021 Challenge on Video Super-Resolution36
This paper presents evaluation results from two competition tracks as well as the proposed solutions to the NTIRE 2021 Challenge on Video Super-Resolution, and develops conventional video SR methods focusing on the restoration quality.
- ADRN: Attention-Based Deep Residual Network for Hyperspectral Image Denoising36
This paper proposes an attention-based deep residual network to directly learn a mapping from noisy HSI to the clean one, and demonstrates that the proposed ADRN scheme outperforms the state-of-the-art methods both in quantitative and visual evaluations.
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- Dilated projection correction network based on autoencoder for hyperspectral image super-resolution31
This paper transforms the HSI SR problem from the image domain into the abundance domain by the dilated projection correction network with an autoencoder, termed as aeDPCN, and preserves the spectral information in the decoder network, which could largely reduce the computational complexity.
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- Ensemble Super-Resolution With a Reference Dataset30
This paper presents a simple but effective single image SR method based on ensemble learning, which can produce a better performance than that could be obtained from any of SR methods to be ensembled (or called component super-resolvers).
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- DANet: Image Deraining via Dynamic Association Learning28
This work proposes a dynamic associated network (DANet) to achieve the association learning between rain streak removal and background recovery, and introduces the parametric association constraint for enhancing the compatibility of deraining model with background reconstruction.
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- Virtual Immunohistochemistry Staining for Histological Images Assisted by Weakly-supervised Learning19
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- MIPI 2024 Challenge on Few-shot RAW Image Denoising: Methods and Results15
The developed solutions in this challenge achieved state-of-the-art performance on Few-shot RAW Image Denoising and were summarized and reviewed in this paper.
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- Multi-Stage Degradation Homogenization for Super-Resolution of Face Images With Extreme Degradations15
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- Statistical Inference of Gaussian-Laplace Distribution for Person Verification13
Experiments conducted on a metric learning method by exploiting Gaussian-Laplace distribution statistical inference show its superiority in terms of effectiveness and efficiency as compared with the state-of-the-art approaches, no matter the appearance description is handcrafted or deep learned.
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- Rep-Mamba: Re-Parameterization in Vision Mamba for Lightweight Remote Sensing Image Super-Resolution10
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- Face hallucination through differential evolution parameter map learning with facial structure prior6
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- Person Re-identification Using Data-Driven Metric Adaptation5
This paper proposes a data-driven metric adaptation method that re-exploits the training data with cross-view consistency to adaptively adjust the metric.
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- Event-Oriented Wiki Document Generation2
The proposed WikiGen model, which consists of two parts, is capable of generating fluent and comprehensive Wikipedia documents and outperforms previous work, achieving state-of-the-art ROUGE scores.
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- IEEE Access Special Section Editorial: Advanced Data Analytics for Large-Scale Complex Data Environments1
Novel data analytics methods are needed to discover meaningful knowledge in advanced applications from objects with large-scale characteristics, such as chemical pounds in bio-pharmacy, brain regions in brain networks and users in social networks.
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- M2DAO-Talker: Harmonizing Multi-Granular Motion Decoupling and Alternating Optimization for Talking-Head Generation–
M2DAO-Talker is proposed, a novel framework integrating Multi-granular Motion decoupling and Dynamic Alternating Optimization that significantly outperforms state-of-the-art approaches both qualitatively and quantitatively.
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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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