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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.
- When Image Denoising Meets High-Level Vision Tasks: A Deep Learning Approach248
This paper proposes a convolutional neural network for image denoising which achieves the state-of-the-art performance and proposes a deep neural network solution that cascades two modules for image Denoising and various high-level tasks, respectively, and uses the joint loss for updating only the denoise network via back-propagation.
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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.
- Connecting Image Denoising and High-Level Vision Tasks via Deep Learning204
A convolutional neural network in which convolutions are conducted in various spatial resolutions via downsampling and upsampling operations in order to fuse and exploit contextual information on different scales and the benefit of exploiting image semantics simultaneously for image denoising and high-level vision tasks via deep learning is demonstrated.
- 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.
- 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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- 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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- Single Image Blind Deblurring Using Multi-Scale Latent Structure Prior94
It is observed that a coarse enough image down-sampled from a blurry observation is approximately a low-resolution version of the latent sharp image, and the proposed algorithm is extended to solve more challenging non-uniform blind image deblurring problem.
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- Self-inspired learning for denoising live-cell super-resolution microscopy86
It is shown that SN2N improves photon efficiency by one-to-two orders of magnitude and is compatible with multiple imaging modalities for volumetric, multicolor, time-lapse SR microscopy.
- 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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- Graph Signal Processing for Geometric Data and Beyond: Theory and Applications82
This work provides the first timely and comprehensive overview of GSP methodologies for geometric data in a unified manner by bridging the connections between geometric data and graphs, among the various geometric data modalities, and with spectral/nodal graph filtering techniques.
- 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.
- 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.
- Deep Lossy Plus Residual Coding for Lossless and Near-Lossless Image Compression72
This paper proposes a unified and powerful deep lossy plus residual (DLPR) coding framework for both lossless and near-lossless image compression, and solves the joint lossy and residual compression problem in the approach of VAEs.
- 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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- 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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- 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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- Video indexing and recommendation based on affective analysis of viewers47
This paper proposes a novel method to index and recommend videos based on affective analysis, mainly on facial expression recognition of viewers, and draws the affective curve which tells the process of affection changes.
- Quality assessment for real out-of-focus blurred images46
This paper constructs a dedicated out-of-focus blurred image dataset and proposes a specific objective quality metric for the blurred images, which combines image sharpness assessment and saliency-guided pooling strategy and demonstrates the proposed metric highly correlates with human judgements of image quality.
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- Lensfree on-chip microscopy based on dual-plane phase retrieval43
A novel dual-plane phase retrieval algorithm to perform a stable and versatile lensless reconstruction of the dense sample under two intensity measurements and a smoothed but resolution-preserving result can be achieved.
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- 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.
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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.
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- 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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- Parametric local multiview hamming distance metric learning31
A new method called Parametric Local Multiview Hamming metric (PLMH), which learns multiview metric based on a set of local hash functions to locally adapt to the data structure of each modality for cross-view similarity search.
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- Cross-media manifold learning for image retrieval & annotation27
A manifold-based cross-media optimization scheme to achieve visual & textual fusion within a unified framework and presents the Isomorphic Manifold Learning to map the annotation affection in image visual space onto keyword semantic space by manifold shrinkage.
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- Inter-block consistent soft decoding of JPEG images with sparsity and graph-signal smoothness priors26
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- Unsupervised fast anomaly detection in crowds23
As the only unsupervised approach, this method outputs competitive results with near real-time processing speed and is compared against several state-of-the-art approaches on UCSD dataset.
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- Context-Aware Semi-Local Feature Detector18
A context-aware semi-local detector (CASL) framework that integrates the context of interest points to recurrently refine their detections and produces more category-aware features which substantially benefits the subsequent visual categorization process.
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- Where should I stand? Learning based human position recommendation for mobile photographing15
This paper proposes a learning-based method to summarize the photographing knowledge from massive social images to improve the robustness and effectiveness and proposes a 3D reconstruction- based method to align the background region and human region into a uniform coordinate system.
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- Place retrieval with graph-based place-view model10
A novel unsupervised framework for efficient place retrieval in movies and sitcoms is presented, leveraging face detection to filter out close-up frames from video dataset, and adopting saliency map analysis to partition background places from foreground actions.
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- Learning heterogeneous data for hierarchical web video classification7
An AdaBoost-Like transfer learning algorithm is proposed to transfer the knowledge learned from Flickr images to YouTube video domain and thus it facilitates video classification, and a bottom-up semantic forest of video concepts by learning from meta-data is constructed.
- Attention-driven action retrieval with DTW-based 3d descriptor matching7
A content-based action retrieval framework to enable effective search of near-duplicated actions in large-scale video database is presented and a prototype system VILAR is presented to facilitate action search within "Friends" soap operas with excellent accuracy, efficiency, and human perception revealing ability.
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- Video stabilization based on saliency driven SIFT matching and discriminative RANSAC5
A modified version of RANSAC method is achieved using the discriminative features to estimate the trajectory of inter-frame motion and reduce the errors caused by the foreground vector.
- A robust texture descriptor using multifractal analysis with Gabor filter5
A novel robust texture descriptor on variance in rotation, scale and illumination, which combines the dominant orientation analysis and multifractal analysis base on Gabor filter is proposed.
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- Bidirectional-isomorphic manifold learning at image semantic understanding & representation4
A Bidirectional- Isomorphic Manifold learning strategy to optimize both visual feature space and textual space, in order to achieve more accurate comprehension for image semantics and relationships and promising results show that the model attains a significant improvement over state-of-the-art algorithms.
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- VisualCor system4
This work presents a context-based actor correlations graph search framework for TV series with excellent ranking accuracy, efficiency, and the ability to reveal human perception, and deploys it onto an online actor correlation search system, VisualCor, which could be viewed as a visualized, content-based simulation of the Renlifang search engine.
- Real-time image annotation by manifold-based biased Fisher discriminant analysis4
A novel Manifold-based Biased Fisher Discriminant Analysis (MBFDA) algorithm to address the Small Sample Size (3S) problem in keyword classifier/model learning and keyword filtering and real-time annotation accuracy with a large margin.
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- A rotation and scale invariant texture description approach3
A novel Local Haar Binary Pattern based framework to ensure invariance in global rotation, scale, and light change, and Evaluation results on Outex and Brodatz databases illustrate the significant advantages of the proposed approach over existing algorithms.
- Text Particles Multi-band Fusion for Robust Text Detection3
A robust text detection method based on text particles (TP) multi-band fusion to solve problems of weak contrast and text-background variance in state-of-the-art text detection algorithms.
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- Visual topic model for web image annotation1
A novel Visual Topic Model framework is proposed, via unsupervised clustering techniques, that aims at analyzing image semantics fusing both content and context, by considering tag correlations and ambiguities.
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- A spatiotemporal context phrase description for general dynamic texture–
This paper proposes a novel dynamic texture description method base on spatiotemporal context phrase for general dynamic texture, and presents a space time constraint and salience rank strategies to extract the representative interest points.
- Contextual dictionaries for image super resolution–
Through adopting discriminative features instead of common features, the method train and use contextual dictionary in the SR process and uses the first-order and second-order gradients of patch as representation, which ensures the neighbor information is introduced in theSR processing.
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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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