Conditional generative adversarial networks (cGANs) aim to synthesize diverse images given the input conditions and the latent codes, but they are prone to map an input to a single output regardless of the variations in latent code, which is also well known as the mode collapse problem of cGANs. To alleviate the problem, in this paper, we investigate explicitly enhancing the statistical dependency between the latent code and the synthesized image in cGANs by utilizing mutual information neural estimators to estimate and maximize the conditional mutual information (CMI) between them given the input condition. The method provides a new perspective from information theory to improve diversity for cGANs and can facilitate many existing conditional image synthesis frameworks with a simple neural estimator extension. Moreover, our studies show that several key designs, including the neural estimator choice, the neural estimator’s network design, and the sampling strategy, are crucial to the success of the method. Extensive experiments on four popular conditional image synthesis tasks, including class-conditioned image generation, paired and unpaired image-to-image translation, and text-to-image generation, demonstrate the effectiveness and superiority of the proposed method.

Arbitrary style transfer (AST) transfers arbitrary artistic styles onto content images. Despite the recent rapid progress, existing AST methods are either incapable or too slow to run at ultra-resolutions (e.g., 4K) with limited resources, which heavily hinders their further applications. In this paper, we tackle this dilemma by learning a straightforward and lightweight model, dubbed MicroAST. The key insight is to completely abandon the use of cumbersome pre-trained Deep Convolutional Neural Networks (e.g., VGG) at inference. Instead, we design two micro encoders (content and style encoders) and one micro decoder for style transfer. The content encoder aims at extracting the main structure of the content image. The style encoder, coupled with a modulator, encodes the style image into learnable dual-modulation signals that modulate both intermediate features and convolutional filters of the decoder, thus injecting more sophisticated and flexible style signals to guide the stylizations. In addition, to boost the ability of the style encoder to extract more distinct and representative style signals, we also introduce a new style signal contrastive loss in our model. Compared to the state of the art, our MicroAST not only produces visually superior results but also is 5-73 times smaller and 6-18 times faster, for the first time enabling super-fast (about 0.5 seconds) AST at 4K ultra-resolutions.

This paper presents a new adversarial training framework for image inpainting with segmentation confusion adversarial training (SCAT) and contrastive learning. SCAT plays an adversarial game between an inpainting generator and a segmentation network, which provides pixel-level local training signals and can adapt to images with free-form holes. By combining SCAT with standard global adversarial training, the new adversarial training framework exhibits the following three advantages simultaneously: (1) the global consistency of the repaired image, (2) the local fine texture details of the repaired image, and (3) the flexibility of handling images with free-form holes. Moreover, we propose the textural and semantic contrastive learning losses to stabilize and improve our inpainting model's training by exploiting the feature representation space of the discriminator, in which the inpainting images are pulled closer to the ground truth images but pushed farther from the corrupted images. The proposed contrastive losses better guide the repaired images to move from the corrupted image data points to the real image data points in the feature representation space, resulting in more realistic completed images. We conduct extensive experiments on two benchmark datasets, demonstrating our model's effectiveness and superiority both qualitatively and quantitatively.

This paper presents a new task for CLIP-based reference-guided facial image semantic transfer: the source facial image is translated to the output image with the high-level semantic attributes from the reference image while maintaining identity preservation. To this end, we employ the powerful generative capability of StyleGAN generator and the rich semantic knowledge of CLIP encoder to accomplish such a task. Additionally, a novel contrastive loss is designed to comprehensively explore the rich semantic information of CLIP for facial semantic concepts. This loss guides the semantic transfer toward desired directions from different perspectives in the pre-defined CLIP space. Besides, a simple yet effective semantic-preserved modulation module is proposed to explicitly map CLIP embeddings of reference image to the latent space. Experiments demonstrate that our approach achieves realistic facial image semantic transfer driven by reference images with various facial semantics.

In this paper, we present the texture reformer, a fast and universal neural-based framework for interactive texture transfer with user-specified guidance. The challenges lie in three aspects: 1) the diversity of tasks, 2) the simplicity of guidance maps, and 3) the execution efficiency. To address these challenges, our key idea is to use a novel feed-forward multi-view and multi-stage synthesis procedure consisting of I) a global view structure alignment stage, II) a local view texture refinement stage, and III) a holistic effect enhancement stage to synthesize high-quality results with coherent structures and fine texture details in a coarse-to-fine fashion. In addition, we also introduce a novel learning-free view-specific texture reformation (VSTR) operation with a new semantic map guidance strategy to achieve more accurate semantic-guided and structure-preserved texture transfer. The experimental results on a variety of application scenarios demonstrate the effectiveness and superiority of our framework. And compared with the state-of-the-art interactive texture transfer algorithms, it not only achieves higher quality results but, more remarkably, also is 2-5 orders of magnitude faster.

Gram-based and patch-based approaches are two important research lines of style transfer. Recent diversified Gram-based methods have been able to produce multiple and diverse stylized outputs for the same content and style images. However, as another widespread research interest, the diversity of patch-based methods remains challenging due to the stereotyped style swapping process based on nearest patch matching. To resolve this dilemma, in this paper, we dive into the crux of existing patch-based methods and propose a universal and efficient module, termed DivSwapper, for diversified patch-based arbitrary style transfer. The key insight is to use an essential intuition that neural patches with higher activation values could contribute more to diversity. Our DivSwapper is plug-and-play and can be easily integrated into existing patch-based and Gram-based methods to generate diverse results for arbitrary styles. We conduct theoretical analyses and extensive experiments to demonstrate the effectiveness of our method, and compared with state-of-the-art algorithms, it shows superiority in diversity, quality, and efficiency.

Artistic style transfer is the task of synthesizing content images with learned artistic styles. Recent studies have shown the potential of Generative Adversarial Networks (GANs) for producing artistically rich stylizations. Despite the promising results, they usually fail to control the generated images' style degree, which is inflexible and limits their applicability for practical use. To address the issue, in this paper, we propose a novel method that for the first time allows adjusting the style degree for existing GAN-based artistic style transfer frameworks in real time after training. Our method introduces two novel modules into existing GAN-based artistic style transfer frameworks: a Style Scaling Injection (SSI) module and a Style Degree Interpretation (SDI) module. The SSI module accepts the value of Style Degree Factor (SDF) as the input and outputs parameters that scale the feature activations in existing models, offering control signals to alter the style degrees of the stylizations. And the SDI module interprets the output probabilities of a multi-scale content-style binary classifier as the style degrees, providing a mechanism to parameterize the style degree of the stylizations. Moreover, we show that after training our method can enable existing GAN-based frameworks to produce over-stylizations. The proposed method can facilitate many existing GAN-based artistic style transfer frameworks with marginal extra training overheads and modifications. Extensive qualitative evaluations on two typical GAN-based style transfer models demonstrate the effectiveness of the proposed method for gaining style degree control for them.

Recent studies have shown remarkable success in universal style transfer which transfers arbitrary visual styles to content images. However, existing approaches suffer from the aesthetic-unrealistic problem that introduces disharmonious patterns and evident artifacts, making the results easy to spot from real paintings. To address this limitation, we propose AesUST, a novel Aesthetic-enhanced Universal Style Transfer approach that can generate aesthetically more realistic and pleasing results for arbitrary styles. Specifically, our approach introduces an aesthetic discriminator to learn the universal human-delightful aesthetic features from a large corpus of artist-created paintings. Then, the aesthetic features are incorporated to enhance the style transfer process via a novel Aesthetic-aware Style-Attention (AesSA) module. Such an AesSA module enables our AesUST to efficiently and flexibly integrate the style patterns according to the global aesthetic channel distribution of the style image and the local semantic spatial distribution of the content image. Moreover, we also develop a new two-stage transfer training strategy with two aesthetic regularizations to train our model more effectively, further improving stylization performance. Extensive experiments and user studies demonstrate that our approach synthesizes aesthetically more harmonious and realistic results than state of the art, greatly narrowing the disparity with real artist-created paintings. Our code is available at https://github.com/EndyWon/AesUST.

Artistic style transfer is an image editing task that aims at repainting everyday photographs with learned artistic styles. Existing methods learn styles from either a single style example or a collection of artworks. Accordingly, the stylization results are either inferior in visual quality or limited in style controllability. To tackle this problem, we propose a novel Dual Style-Learning Artistic Style Transfer (DualAST) framework to learn simultaneously both the holistic artist-style (from a collection of artworks) and the specific artwork-style (from a single style image): the artist-style sets the tone (i.e., the overall feeling) for the stylized image, while the artwork-style determines the details of the stylized image, such as color and texture. Moreover, we introduce a Style-Control Block (SCB) to adjust the styles of generated images with a set of learnable style-control factors. We conduct extensive experiments to evaluate the performance of the proposed framework, the results of which confirm the superiority of our method.

Image style transfer aims to transfer the styles of artworks onto arbitrary photographs to create novel artistic images. Although style transfer is inherently an underdetermined problem, existing approaches usually assume a deterministic solution, thus failing to capture the full distribution of possible outputs. To address this limitation, we propose a Diverse Image Style Transfer (DIST) framework which achieves significant diversity by enforcing an invertible cross-space mapping. Specifically, the framework consists of three branches: disentanglement branch, inverse branch, and stylization branch. Among them, the disentanglement branch factorizes artworks into content space and style space; the inverse branch encourages the invertible mapping between the latent space of input noise vectors and the style space of generated artistic images; the stylization branch renders the input content image with the style of an artist. Armed with these three branches, our approach is able to synthesize significantly diverse stylized images without loss of quality. We conduct extensive experiments and comparisons to evaluate our approach qualitatively and quantitatively. The experimental results demonstrate the effectiveness of our method.

Although existing artistic style transfer methods have achieved significant improvement with deep neural networks, they still suffer from artifacts such as disharmonious colors and repetitive patterns. Motivated by this, we propose an internal-external style transfer method with two contrastive losses. Specifically, we utilize internal statistics of a single style image to determine the colors and texture patterns of the stylized image, and in the meantime, we leverage the external information of the large-scale style dataset to learn the human-aware style information, which makes the color distributions and texture patterns in the stylized image more reasonable and harmonious. In addition, we argue that existing style transfer methods only consider the content-to-stylization and style-to-stylization relations, neglecting the stylization-to-stylization relations. To address this issue, we introduce two contrastive losses, which pull the multiple stylization embeddings closer to each other when they share the same content or style, but push far away otherwise. We conduct extensive experiments, showing that our proposed method can not only produce visually more harmonious and satisfying artistic images, but also promote the stability and consistency of rendered video clips.

MOTIVATION: Ion mobility spectrometry (IMS) separations are increasingly used in conjunction with mass spectrometry (MS) for separation and characterization of ionized molecular species. Information obtained from IMS measurements includes the ion's collision cross section (CCS), which reflects its size and structure and constitutes a descriptor for distinguishing similar species in mixtures that cannot be separated using conventional approaches. Incorporating CCS into MS-based workflows can improve the specificity and confidence of molecular identification. At present, there is no automated, open-source pipeline for determining CCS of analyte ions in both targeted and untargeted fashion, and intensive user-assisted processing with vendor software and manual evaluation is often required. RESULTS: We present AutoCCS, an open-source software to rapidly determine CCS values from IMS-MS measurements. We conducted various IMS experiments in different formats to demonstrate the flexibility of AutoCCS for automated CCS calculation: (i) stepped-field methods for drift tube-based IMS (DTIMS), (ii) single-field methods for DTIMS (supporting two calibration methods: a standard and a new enhanced method) and (iii) linear calibration for Bruker timsTOF and non-linear calibration methods for traveling wave based-IMS in Waters Synapt and Structures for Lossless Ion Manipulations. We demonstrated that AutoCCS offers an accurate and reproducible determination of CCS for both standard and unknown analyte ions in various IMS-MS platforms, IMS-field methods, ionization modes and collision gases, without requiring manual processing. AVAILABILITY AND IMPLEMENTATION: https://github.com/PNNL-Comp-Mass-Spec/AutoCCS. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Demo datasets are publicly available at MassIVE (Dataset ID: MSV000085979).

The image generation model based on generative adversarial networks has recently received significant attention and can produce diverse, sharp, and realistic images. However, generating high-resolution images has long been a challenge. In this paper, we propose a progressive spatial recursive adversarial expansion model(called SpatialGAN) capable of producing high-quality samples of the natural image. Our approach uses a cascade of convolutional networks to progressively generate images in a part-to-whole fashion. At each level of spatial expansion, a separate image-to-image spatial adversarial expansion network (conditional GAN) is recursively trained based on context image generated by previous GAN or CGAN. Unlike other coarse-to-fine generative methods that constraint on generative process either by multi-scale resolution or by hierarchical feature, the SpatialGAN decomposes image space into multiple subspaces and gradually resolves uncertainties in the local-to-whole generative process. The SpatialGAN greatly stabilizes and speeds up the training, which allows us to produce images of high quality. Based on visual Inception Score and Fréchet Inception Distance, we demonstrate that the quality of images generated by SpatialGAN on several typical datasets is better than that of images generated by GANs without cascading and comparative with the state of art methods with cascading.

The recent research of hyperspectral images(HSIs) classification depicts that taking spectral-spatial features into account can considerably improve accuracy. Since HSI is a 3D cube datum, amounts of 3D network structures emerged to extract spectral-spatial features consecutively. In this paper, we present a simple spectral-spatial classification framework(SSP3DNet) based on densely connected structure with Pseudo-3D block for HSIs. Firstly, a data augmentation strategy was implemented to solve the problem of limited and uneven training samples in the data preprocessing step. Secondly, the Pseudo-3D block can capture both spectral and spatial features simultaneously which is more economic by decreasing the number of parameters compared with traditional 3D convolution network. Then DenseNet learning framework is utilized to ease the training of networks as well as improving the classification performance. Especially, to prevent over-fitting, some tricks like early stopping, LeakyReLu, batch normalization, and dropout layers are used in our SSP3DNet which enable the SSP3DNet to obtain accuracy within 80 epochs. Experimental results on two well-known hyperspectral datasets show that the proposed SSP3DNet method achieves the best classification accuracy in comparison with lately traditional and deep-learning-based methods.

Integrating spatial information into the sparse subspace clustering (SSC) models for hyperspectral images (HSIs) is an effective way to improve clustering accuracy. Since HSI is a three-dimensional (3D) cube datum, 3D spectral-spatial filtering becomes a simple method for extracting the spectral-spatial information. In this paper, a novel spectral-spatial SSC framework based on 3D edge-preserving filtering (EPF) is proposed to improve the clustering accuracy of HSI. First, the initial sparse coefficient matrix is obtained in the sparse representation process of the classical SSC model. Then, a 3D EPF is conducted on the initial sparse coefficient matrix to obtain a more accurate coefficient matrix by solving an optimization problem based on ADMM, which is used to build the similarity graph. Finally, the clustering result of HSI data is achieved by applying the spectral clustering algorithm to the similarity graph. Specifically, the filtered matrix can not only capture the spectral-spatial information but the intensity differences. The experimental results on three real-world HSI datasets demonstrated that the potential of including the proposed 3D EPF into the SSC framework can improve the clustering accuracy.

Matrix factorization is a popular low dimensional representation approach that plays an important role in many pattern recognition and computer vision domains. Among them, convex and semi-nonnegative matrix factorizations have attracted considerable interest, owing to its clustering interpretation. On the other hand, the generalized correlation function (correntropy) as the error measure does not depend on the assumption of Gaussianity, which the mean square error (MSE) heavily depends on. In this paper, we propose two novel algorithms, called Maximum Correntropy Criterion based Convex and Semi-Nonnegative Matrix Factorization (MCC-ConvexNMF, MCC-SemiNMF). Compared with the mean square error based convex and semi-nonnegative matrix factorization, the proposed methods can extract more information from the data and produce more accurate solutions. Experimental results on both synthetic dataset and the popular face database illustrate the effectiveness of our methods.