Is this you? Claim this profile to correct it, add a bio and choose the work people see first.
Claim this profileWorks250 from public data
- 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.
- 762
- 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.
- 360
- 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.
- 214
- 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.
- 212
- 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
- 153
- 139
- 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.
- 123
- 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.
- 101
- 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.
- 99
- 99
- 92
- 92
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.
Report an error
Wrong papers, two people merged into one, or a profile that should not be here? Tell us and we will fix or hide it.