Researchers have recently proposed arbitrary style transfer methods based on various model frameworks. Although all of them have achieved good results, they still face the problems of insufficient stylization, artifacts and inadequate retention of content structure. In order to solve these problems, we propose a flow style-aware network (FSANet) for arbitrary style transfer, which combines a VGG network and a flow network. FSANet consists of a flow style transfer module (FSTM), a dynamic regulation attention module (DRAM), and a style feature interaction module (SFIM). The flow style transfer module uses the reversible residue block features of the flow network to create a sample feature containing the target content and style. To adapt the FSTM to VGG networks, we design the dynamic regulation attention module and exploit the sample features both at the channel and pixel levels. The style feature interaction module computes a style tensor that optimizes the fused features. Extensive qualitative and quantitative experiments demonstrate that our proposed FSANet can effectively avoid artifacts and enhance the preservation of content details while migrating style features.

Abstract Monocular depth estimation (MDE) has made great progress with the development of convolutional neural networks (CNNs). However, these approaches suffer from essential shortsightedness due to the utilization of insufficient feature-based reasoning. To this end, we propose an effective parallel CNNs and Transformer model for MDE via dual attention (PCTDepth). Specifically, we use two stream backbones to extract features, where ResNet and Swin Transformer are utilized to obtain local detail features and global long-range dependencies, respectively. Furthermore, a hierarchical fusion module (HFM) is designed to actively exchange beneficial information for the complementation of each representation during the intermediate fusion. Finally, a dual attention module is incorporated for each fused feature in the decoder stage to improve the accuracy of the model by enhancing inter-channel correlations and focusing on relevant spatial locations. Comprehensive experiments on the KITTI dataset demonstrate that the proposed model consistently outperforms the other state-of-the-art methods.

Multi-modality complementary information brings new impetus and innovation to saliency object detection (SOD). However, most existing RGB-D SOD methods either indiscriminately handle RGB features and depth features or only take depth features as additional information of RGB subnet-work, ignoring the different roles of two modalities for SOD tasks. To tackle this issue, we propose a novel multi-modality diversity fusion network with SwinTransformer (M2DFNet) for RGB-D SOD from the perspective of the different status of multi-modality, which adequately explores the roles of RGB and depth modalities. To this end, a triple-diversity supervision mechanism (TDSM) and a diversity fusion module (DFM) are designed to parse the function of different modalities. Besides, we designed a dense decoder (DSD) to integrate multi-scale features and transfer gain information from top to bottom, which can improve the performance of SOD. Extensive experiments on five benchmark datasets demonstrate that the proposed M2DFNet outperforms 17 other state-of-the-art (SOTA) RGB-D SOD methods.

Multi-scale context is crucial for the accurate salient object detection (SOD) in the real-world scenes. Although current contextual information-based SOD methods have achieved great progress, they may fail to generate precise saliency maps due to their seldom considering the correlation of different scale context during the extraction process. To address these issues, we propose an Efficient Multi-Scale Context Exploration Network (EMCENet) for SOD. Specifically, a progressive multi-scale context extraction (PMCE) module is designed to progressively capture strongly correlated multi-scale context by using multi-receptive-field convolution operations. Afterwards, a hierarchical feature hybrid interaction (HFHI) module is introduced to generate powerful feature representations by adaptively aggregating multi-level features in a hybrid interaction strategy. Extensive experimental results on six public datasets demonstrate that the proposed EMCENet method without any post-processing performs favorably against 13 state-of-the-art SOD methods.

To balance the performance of chaos and the algorithm’s efficiency, a Henon-Modulated-Iterative (HMI) map is constructed in this paper. And the chaotic characteristics of the HMI map are analyzed from three aspects: bifurcation map, Lyapunov exponent and balance degree. On this basis, an image encryption algorithm based on a convolutional neural network (CNN) and four square matrix encoding is proposed. Then, the random sequence generation module is designed using CNN, and the generated random sequence is used to construct the scrambling coordinate matrix and modify the position of the pixel values. Finally, the scrambled image is diffused by four square matrix encoding so that the pixel values are evenly distributed, while non-sequential diffusion using hexadecimal addition and subtraction rules improves the security of the encryption algorithm. Experimental results show that the encryption algorithm proposed in this paper has a good encryption effect and can resist various attacks.

The peptide therapeutics market is providing new opportunities for the biotechnology and pharmaceutical industries. Therefore, identifying therapeutic peptides and exploring their properties are important. Although several studies have proposed different machine learning methods to predict peptides as being therapeutic peptides, most do not explain the decision factors of model in detail. In this work, an Interpretable Therapeutic Peptide Prediction (ITP-Pred) model based on efficient feature fusion was developed. First, we proposed three kinds of feature descriptors based on sequence and physicochemical property encoded, namely amino acid composition (AAC), group AAC and coding autocorrelation, and concatenated them to obtain the feature representation of therapeutic peptide. Then, we input it into the CNN-Bi-directional Long Short-Term Memory (BiLSTM) model to automatically learn recognition of therapeutic peptides. The cross-validation and independent verification experiments results indicated that ITP-Pred has a higher prediction performance on the benchmark dataset than other comparison methods. Finally, we analyzed the output of the model from two aspects: sequence order and physical and chemical properties, mining important features as guidance for the design of better models that can complement existing methods.

This paper proposes an unsupervised bottom-up saliency detection approach by exploiting novel background template and foreground information. First, a discriminative feature vector is extracted from each super-pixel to cover regional color, contrast and texture information. Then we apply it to get a background based saliency map based on a background template. In order to get more accurate saliency map, we select highly confident compact foreground seeds to compute a foreground based saliency map. After fusing the two saliency maps, the integrated map is refined to achieve the final result. Experimental results show that the proposed algorithm generates high-quality saliency maps against the state-off-the-art saliency detection methods on four publicly available datasets.

In this study, the authors propose a distinctive bottom‐up visual saliency detection algorithm based on a new background prior and a new reinforcement. Inspired by genetic algorithm, the final map is obtained with three steps. First of all, the authors construct a background‐based saliency map by manifold ranking via superior image corners selected by convex‐hull as background prior, which is different from most of the existing background prior‐based methods treated all image boundaries as background. Then, a better result is obtained by ranking the relevance of the image elements with foreground seeds extracted from the preliminary saliency map. Furthermore, a novel optimisation framework is introduced with the intention of refining the map, which integrates an energy function with a guided filter. Experimental results on three public datasets indicate that the proposed method performs favourably against the state‐of‐the‐art algorithms.