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Recent years have witnessed the remarkable performance of diffusion models in various vision tasks. However, for image restoration that aims to recover clear images with sharper details from given degraded observations, diffusion-based methods ...

• Simple constraining noises cannot effectively learn complex degradation information.
• Embed diffusion into Transformer to model long-range dependencies to promote recovery.
• Coarse-to-Fine training improves quality affected by ...

In order to improve the quality of synthesized videos, currently, one predominant method involves retraining an expert diffusion model and then implementing a noising-denoising process for refinement. Despite the significant training costs, ...

Multi-view subspace clustering has attracted extensive attention due to its ability to efficiently handle data from diverse sources. In recent years, plentiful multi-view subspace clustering methods have emerged and achieved satisfactory ...

Existing super-resolution methods exhibit limitations when applied to nighttime scenes, primarily due to their lack of adaptation to low-pair dynamic range and noise-heavy dark-light images. In response, this paper introduces an innovative customized ...

Multi-view subspace clustering has recently received great interest in the communities of computer vision and pattern recognition, as it can overcome the limitation of conventional single-view subspace clustering by fusing complementary ...

Autonomous vehicles and mobile robotics usually rely on LiDAR sensors for outdoor environment perception. Airborne particles, such as fog, rain, and snow, introduce undesired measurement points resulting in missing detection and false positives. ...${}_{\mathrm{}}$${}_{\mathrm{}}$${}_{\mathrm{}}$${}_{\mathrm{}}$${}_{\mathrm{}}$

• A novel single-stage multi-person pose estimator.
• Better strategies to identify positive poses.
• A pose scoring module to enhance pose selection in NMS.
• Outperforms existing single-stage methods.
• Competitive with the latest ...

Existing multi-person pose estimators can be roughly divided into two-stage approaches (top-down and bottom-up approaches) and one-stage approaches. The two-stage methods either suffer high computational redundancy for additional person detectors ...

Most conventional dehazing methods focus on separately estimating key parameters (e.g., the transmission map and the atmospheric light) based on the atmospheric scattering model to generate haze-free images, which may face the limitation of error ...

The multi-view subspace clustering attracts much attention in recent years. Most methods follow the framework of fusing the affinity graph learned in each view. In this framework, both the fusion strategy and built graph of each view are very important. ...

Multi-view subspace clustering has received widespread attention. Since data may violate the linear assumption in many practical applications, multi-view subspace clustering methods based on deep neural network are developed in recent years. ...

Single image deraining is a highly ill-posed problem. Existing deep neural network-based algorithms usually use larger deep models to solve this problem, which is less effective and efficient. In this paper, we propose a deep neural network based ...

Rain removal is an important but challenging computer vision task as rain streaks can severely degrade the visibility of images that may make other visions or multimedia tasks fail to work. Previous works mainly focused on feature extraction and ...

In the field of multimedia, single image deraining is a basic pre-processing work, which can greatly improve the visual effect of subsequent high-level tasks in rainy conditions. In this paper, we propose an effective algorithm, called JDNet, to solve ...

Raindrop blur or rain streaks can severely degrade the visual quality of the images, which causes many practical vision systems to fail to work, such as autonomous driving and video surveillance. Hence, it is important to address the problem of ...

Rainy images severely degrade the visibility and make many computer vision algorithms invalid. Hence, it is necessary to remove rain streaks from single image. In this paper, we propose a novel network to handle with single image de-raining, which ...

Spectral clustering-based subspace clustering methods have attracted broad interest in recent years. This kind of methods usually uses the self-representation in the original space to extract the affinity between the data points. However, we can ...

Rank minimization is a key component of many computer vision and machine learning methods, including robust principal component analysis (RPCA) and low-rank representations (LRR). However, usual methods rely on optimization to produce a point ...

• We propose pGDP for rank penalization.
• We derive Bayesian versions of RPCA and LRR with pGDP prior.
• The fixed hyper-prior specification can result in good performance widely.
• Our methods have generality for rank penalization ...

Feature and saliency analyses are crucial for various graphics applications. The key idea is to automatically compute and recommend the salient or outstanding regions of concerned models. However, there is no universally-applicable criterion for ...

• Propose an efficient framework to recommend interest region on arbitrary model.
• Formulate the task as PDEs-directed graph diffusion using a small training set.
• Exploit submodular optimization and guidance for adaptive and learnable ...

Existing deblurring methods mainly focus on developing effective image priors and assume that blurred images contain insignificant amounts of noise. However, state-of-the-art deblurring methods do not perform well on real-world images degraded ...${}_{\mathrm{}}$${}_{\mathrm{}}$

This article describes how attractive structures are always correspond to objects of interest in human perception, thus extracting attractive structures is a fundamental problem in many image analysis tasks, which is of great practical importance. In ...