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- Contrastive Learning for Compact Single Image Dehazing926
This paper proposes a novel contrastive regularization (CR) built upon contrastive learning to exploit both the information of hazy images and clear images as negative and positive samples, respectively and develops a compact dehazing network based on autoencoder-like framework.
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- On Unifying Multi-view Self-Representations for Clustering by Tensor Multi-rank Minimization505
The proposed method has achieved highly competent objective performance compared to several state-of-the-art multi-view clustering methods and its minimization problem can be efficiently solved with theoretical convergence guarantee and relatively low computational complexity.
- LatticeNet: Towards Lightweight Image Super-Resolution with Lattice Block367
A lightweight SR model, LatticeNet, is proposed, which uses series connection of LBs and the backward feature fusion, which can achieve superior accuracy on four available benchmark datasets against other state-of-the-art methods, while maintaining relatively low computation and memory requirements.
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- Advancing Image Understanding in Poor Visibility Environments: A Collective Benchmark Study351
The UG2+ challenge Track 2 competition in IEEE CVPR 2019 is launched, aiming to evoke a comprehensive discussion and exploration about whether and how low-level vision techniques can benefit the high-level automatic visual recognition in various scenarios.
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- PromptAD: Learning Prompts with only Normal Samples for Few-Shot Anomaly Detection212
A one-class prompt learning method for few-shot anomaly detection, termed PromptAD, which proposes semantic concatenation which can transpose normal prompts into anomaly prompts by concatenating normal prompts with anomaly suffixes, and introduces the concept of explicit anomaly margin.
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- Farewell to Mutual Information: Variational Distillation for Cross-Modal Person Re-Identification172
A new strategy, Variational Self-Distillation (VSD), is presented, which provides a scalable, flexible and analytic solution to essentially fitting the mutual information but without explicitly estimating it, and highlights the need to rethink the way to estimate mutual information.
- Hyperspectral Image Restoration via Iteratively Regularized Weighted Schatten $p$ -Norm Minimization162
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- COTR: Compact Occupancy TRansformer for Vision-Based 3D Occupancy Prediction126
The Compact Occupancy TRansformer (COTR) is proposed, with a geometry-aware occupancy encoder and a semantic-aware group decoder to reconstruct a compact 3D OCC representation, and shows evident performance gains across multiple baselines.
- Weakly Supervised Semantic Segmentation for Large-Scale Point Cloud125
An effective weakly supervised method containing two components to solve the problem of ineffective learning of network for large-scale point cloud semantic segmentation, and a sparse label propagation mechanism is proposed with the help of generated class prototypes which is used to measure the classification confidence of unlabeled point.
- NTIRE 2020 Challenge on NonHomogeneous Dehazing125
This paper reviews the NTIRE 2020 Challenge on Non-Homogeneous Dehazing of images (restoration of rich details in hazy image) and proposed solutions gauge the state-of-the-art in image dehazing.
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- Underwater-GAN: Underwater Image Restoration via Conditional Generative Adversarial Network107
This paper proposes Underwater-GAN, a conditional generative adversarial network for underwater image restoration, which uses Wasserstein GAN with gradient penalty term as the backbone network and designs the loss function as the sum of the loss of generative adversary network and the perceptual loss.
- AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results101
The AIM 2020 challenge on efficient single image super-resolution was to super-resolve an input image with a magnification factor x4 based on a set of prior examples of low and corresponding high resolution images with focus on the proposed solutions and results.
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- Not All Pixels Are Matched99
A dense contrastive learning framework (DCLNet) is proposed, which performs pixel-to-pixel dense alignment acting on the intermediate representations, rather than the final deep feature, reducing the modality gap from a new perspective.
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- Unveiling the Power of CLIP in Unsupervised Visible-Infrared Person Re-Identification85
A new prompt learning paradigm for unsupervised visible-infrared person re-identification (USL-VI-ReID) by taking full advantage of the visual-text representation ability from CLIP is proposed and a new memory-swapping contrastive learning is proposed.
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- Comprehensive Regularization in a Bi-directional Predictive Network for Video Anomaly Detection78
A novel bi-directional architecture with three consistency constraints to comprehensively regularize the prediction task from pixel-wise, cross-modal, and temporal-sequence levels to outperforms advanced anomaly detectors and achieves state-of-the-art results.
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- Boundary-Aware Geometric Encoding for Semantic Segmentation of Point Clouds69
A Boundary Prediction Module (BPM) to predict boundary points and a boundary-aware Geometric Encoding Module (GEM) is designed to encode geometric information and aggregate features with discrimination in a neighborhood, so that the local features belonging to different categories will not be polluted by each other.
- Uncertainty-Driven Dehazing Network65
A novel uncertainty-driven dehazing network (UDN) is proposed that improves the dehazed results by exploiting the relationship between the uncertain and confident representations, and develops an uncertainty- driven self-distillation loss to improve the uncertain representation by transferring the knowledge from the confident one.
- Towards Compact Single Image Super-Resolution via Contrastive Self-distillation61
A novel contrastive self-distillation (CSD) framework to simultaneously compress and accelerate various off-the-shelf SR models to improve the quality of SR images and PSNR/SSIM via explicit knowledge transfer is proposed.
- Omni-supervised Point Cloud Segmentation via Gradual Receptive Field Component Reasoning60
This paper brings the first omni-scale supervision method to point cloud segmentation via the proposed gradual Receptive Field Component Reasoning (RFCR) and brings new state-of-the-art performances for S3DIS as well as Semantic3D and ranks the 1st in the ScanNet benchmark among all the point-based methods.
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- CLIP-Guided Federated Learning on Heterogeneity and Long-Tailed Data58
This paper uses CLIP to optimize the federated learning between server and client models under its vision-language supervision, and proposes the CLIP-guided FL (CLIP2FL) method, which achieves impressive performance and effectively deals with data heterogeneity and long-tail distribution.
- AIM 2019 Challenge on Image Demoireing: Methods and Results57
This paper reviews the first-ever image demoireing challenge that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ICCV 2019, and focuses on the proposed solutions and their results.
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- NTIRE 2020 Challenge on Image Demoireing: Methods and Results45
This paper reviews the Challenge on Image Demoireing that was part of the New Trends in Image Restoration and Enhancement (NTIRE) workshop, held in conjunction with CVPR 2020, and examines the current state-of-the-art in image and burst image demoireing problems.
- Patch Proposal Network for Fast Semantic Segmentation of High-Resolution Images44
A patch proposal network (PPN) is introduced, which adaptively distinguishes the critical patches from the trivial ones to fuse with the whole image for refining segmentation and achieves almost the best segmentation performance compared with the state-of-the-art segmentation methods.
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- One-for-More: Continual Diffusion Model for Anomaly Detection41
An iterative singular value decomposition method based on the transitive property of linear representation, which consumes tiny memory and incurs almost no performance loss, and an anomaly-masked network to enhance the condition mechanism of the diffusion model are proposed.
- Cross-Domain and Cross-Modal Knowledge Distillation in Domain Adaptation for 3D Semantic Segmentation41
This paper designs the multi-modal style transfer to convert source image and point cloud to target style and proposes a novel model (Dual-Cross) that integrates Cross-Domain knowledge distillation (CDKD) and Cross-Modal Knowledge Distillation (CMKD), which significantly outperforms both uni- modal and cross-modAL state-of-the-art methods.
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- Discriminative subspace learning with sparse representation view-based model for robust visual tracking38
A robust tracking algorithm to handle drifting problem and address the problem of learning maximum margin projection under the Spectral Regression (SR) which results in significant savings in computational time.
- NTIRE 2022 Challenge on Stereo Image Super-Resolution: Methods and Results35
The 1st NTIRE challenge on stereo image super-resolution (restoration of rich details in a pair of low-resolution stereo images) with a focus on new solutions and results establishes a new benchmark for stereo image SR.
- Image Understands Point Cloud: Weakly Supervised 3D Semantic Segmentation via Association Learning32
This paper designs a dual-branch network equipped with an active labeling strategy to maximize the power of tiny parts of labels and to directly realize 2D-to-3D knowledge transfer and establishes a cross-modal self-training framework, which iterates between parameter updating and pseudolabel estimation.
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- AdaFormer: Efficient Transformer with Adaptive Token Sparsification for Image Super-resolution18
A texture-relevant sparse attention block with parallel global and local branches is introduced, aiming to integrate informative tokens from the global view instead of only in fixed local windows, and an early-exit strategy is designed to progressively halt tokens according to the token importance.
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- Boosting Lightweight Single Image Super-resolution via Joint-distillation16
A novel joint-distillation (JDSR) framework to boost the representation of various off-the-shelf lightweight SR models by a large margin, with the same model architecture and inference expense.
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- Dual-Branch Knowledge Distillation via Residual Features Aggregation Module for Anomaly Segmentation8
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- Adjustable Memory-efficient Image Super-resolution via Individual Kernel Sparsity6
This work essentially provides a dynamic parameter reassignment scheme with a given resource budget for an off-the-shelf SR model and demonstrates that IKS imparts considerable sparsity with negligible effect on SR quality.
- Hierarchical Walking Transformer for Object Re-Identification6
A new hierarchical walking attention is presented, which provides a scalable, flexible, and interpretable sparsification strategy to reduce the complexity from quadratic to linear, and meanwhile evidently boost the performance.
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- BeyondSparse: Facilitating Mamba to Enhance Cross-Domain 3D Semantic Segmentation in Adverse Weather–
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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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