To enhance low-light images to normally-exposed ones is highly ill-posed, namely that the mapping relationship between them one-to-many. Previous works based on pixel-wise reconstruction losses and deterministic processes fail capture complex conditional distribution of normally exposed images, which results in improper brightness, residual noise, artifacts. In this paper, we investigate model one-to-many via a proposed normalizing flow model. An invertible network takes images/features as...

Removing undesired reflections from a photo taken in front of glass is great importance for enhancing the efficiency visual computing systems. Various approaches have been proposed and shown to be visually plausible on small datasets collected by their authors. A quantitative comparison existing using same dataset has never conducted due lack suitable benchmark data with ground truth. This paper presents first captured Single-image Reflection Removal 'SIR2' 40 controlled 100 wild scenes,...

Reflection removal aims at separating the mixture of desired scene and undesired reflections. Locating reflection background edges is a key step for removal. In this paper, we present visual depth guided method to remove Our idea use Depth Field (DoF) label edges. We propose DoF confidence map where pixels with higher values are assumed belong components. Moreover, observe that images different resolutions show properties in map. Thus, introduce multi-scale computing strategy classify edge...

Removing the undesired reflections from images taken through glass is of broad application to various computer vision tasks. Non-learning based methods utilize different handcrafted priors such as separable sparse gradients caused by levels blurs, which often fail due their limited description capability properties real-world reflections. In this paper, we propose Concurrent Reflection Removal Network (CRRN) tackle problem in a unified framework. Our proposed network integrates image...

Removing the undesired reflections from images taken through glass is of broad application to various computer vision tasks. Non-learning based methods utilize different handcrafted priors such as separable sparse gradients caused by levels blurs, which often fail due their limited description capability properties real-world reflections. In this paper, we propose a network with feature-sharing strategy tackle problem in cooperative and unified framework, integrating image context...

Face Anti-Spoofing (FAS) is essential to secure face recognition systems and has been extensively studied in recent years. Although deep neural networks (DNNs) for the FAS task have achieved promising results intra-dataset experiments with similar distributions of training testing data, DNNs' generalization ability limited under cross-domain scenarios different data. To improve ability, hybrid methods explored extract task-aware handcrafted features (e.g., Local Binary Pattern) as...

Recently, we have witnessed great progress in the field of medical imaging classification by adopting deep neural networks. However, recent advanced models still require accessing sufficiently large and representative datasets for training, which is often unfeasible clinically realistic environments. When trained on limited datasets, network lack generalization capability, as data within a certain distribution (e.g. captured device vendor or patient population) may not be able to generalize...

Removing the undesired reflections in images taken through glass is of broad application to various image processing and computer vision tasks. Existing single image-based solutions heavily rely on scene priors such as separable sparse gradients caused by different levels blur, they are fragile when not observed. In this paper, we notice that strong usually dominant a limited region whole image, propose region-aware reflection removal approach automatically detecting heterogeneously regions...

Reflection removal has been discussed for more than decades. This paper aims to provide the analysis different reflection properties and factors that influence image formation, an up-to-date taxonomy existing methods, a benchmark dataset, unified benchmarking evaluations state-of-the-art (especially learning-based) methods. Specifically, this presents SIngle-image Removal Plus dataset "SIR 2+ " with new consideration in-the-wild scenarios glass diverse color unplanar shapes. We further...

Parkinson's disease (PD) is a common degenerative of the nervous system in elderly. The early diagnosis PD very important for potential patients to receive prompt treatment and avoid aggravation disease. Recent studies have found that always suffer from emotional expression disorder, thus forming characteristics "masked faces". Based on this, we propose an auto method based mixed facial expressions paper. Specifically, proposed cast into four steps: Firstly, synthesize virtual face images...

Existing learning-based single image reflection removal methods using paired training data have fundamental limitations about the generalization capability on real-world reflections due to limited variations in pairs. In this work, we propose jointly generate and separate within a weakly-supervised learning framework, aiming model formation more comprehensively with abundant unpaired supervision. By imposing adversarial losses combinable mapping mechanism multi-task structure, proposed...

Heterogeneous Transfer Learning (HTL) aims to solve transfer learning problems where a source domain and target are of heterogeneous types features. Most existing HTL approaches either explicitly learn feature mappings between the domains or implicitly reconstruct cross-domain features based on matrix completion techniques. In this paper, we propose new method deep framework, kernel embedding distributions is trained in an adversarial manner for across domains. We conduct extensive...

To enhance low-light images to normally-exposed ones is highly ill-posed, namely that the mapping relationship between them one-to-many. Previous works based on pixel-wise reconstruction losses and deterministic processes fail capture complex conditional distribution of normally exposed images, which results in improper brightness, residual noise, artifacts. In this paper, we investigate model one-to-many via a proposed normalizing flow model. An invertible network takes images/features as...

Neural Radiance Fields (NeRF) have the potential to be a major representation of media. Since training NeRF has never been an easy task, protection its model copyright should priority. In this paper, by analyzing pros and cons possible solutions, we propose protect models replacing original color in with watermarked representation. Then, distortion-resistant rendering scheme is designed guarantee robust message extraction 2D renderings NeRF. Our proposed method can directly while maintaining...

In this study, we compared the chemical forms and subcellular distribution of Cd in high-Cd (X16) low-Cd (N88) sweet potato cultivars through hydroponic experiments examined their roots by histochemical staining. The results showed that inorganic pectate/protein-integrated predominated leaves, concentrations were significantly higher X16 than N88. However, roots, was mostly integrated with pectate protein, concentration N88 X16. It mainly stored vacuolar sequestration cell wall binding....

Our everyday lives are filled with occlusions that we strive to see through. By aggregating desired background information from different viewpoints, can easily eliminate such without any external occlusion-free supervision. Though several occlusion removal methods have been proposed empower machine vision systems ability, their performances still unsatisfactory due reliance on We propose a novel method for by directly building mapping between position and viewing angles the corresponding...

Removing the undesired moiré patterns from images capturing contents displayed on screens is of increasing research interest, as need for recording and sharing instant information conveyed by growing. Previous demoiréing methods provide limited investigations into formation process to exploit moiré-specific priors guiding learning models. In this paper, we investigate pattern perspective signal aliasing, correspondingly propose a coarse-to-fine disentangling framework. framework, first...

Neural Radiance Fields (NeRF) have been gaining attention as a significant form of 3D content representation. With the proliferation NeRF-based creations, need for copyright protection has emerged critical issue. Although some approaches proposed to embed digital watermarks into NeRF, they often neglect essential model-level considerations and incur substantial time overheads, resulting in reduced imperceptibility robustness, along with user inconvenience. In this paper, we extend previous...

Hateful meme detection aims to prevent the proliferation of hateful memes on various social media platforms. Considering its impact environments, this paper introduces a previously ignored but significant threat detection: backdoor attacks. By injecting specific triggers into samples, attackers can manipulate detector output their desired outcomes. To explore this, we propose Meme Trojan framework initiate attacks detection. involves creating novel Cross-Modal Trigger (CMT) and learnable...