Capsule Network is a novel and promising neural network in the field of deep learning, which has shown good performance in image classification by encoding features into capsules and constructing the part-whole relationships. However, the original Capsule Network is not suitable for the images with complex background due to its weak ability of feature extraction, a large number of training parameters, and the characteristic of explaining everything in the image. To address the above issues, we propose an advanced Capsule Network named RS-CapsNet, which uses Res2Net block to extract multi-scale features, and Squeeze-and-Excitation (SE) block to highlight useful features and suppress useless features. Meanwhile, we adopt the method of linear combination between capsules, which enhances the representation ability of capsules for detected object and reduces the number of capsules. Moreover, we propose the method of firstly constructing intermediate capsules that can represent most of the detected object, and then using the intermediate capsules and primary capsules to construct the classification capsules together. The experimental results show that proposed RS-CapsNet has better performance on CIFAR10, CIFAR100, SVHN, FashionMNIST, and AffNIST datasets, it can also provide better translation equivariance, and the number of training parameters is reduced by 65.11%.

Manual font design is difficult and requires professional knowledge and skills to perform. Therefore, how to automatically generate the required fonts is a very challenging research task. On the other hand, there are few people who have studied the relationship between fonts and emotions, and common fonts generally cannot reflect emotional information. This paper proposes an Emotional Guidance GAN: an automatic Chinese font generation framework based on Generative Adversarial Network (GAN), which enables the generated fonts to reflect human emotional information. First, an elaborated questionnaire system was developed from Tencent company, which aims to quantitatively figure out the relationship between fonts and emotions. A visual expression recognition part is designed based on the trained model to provide a font generation module with conditional information. Moreover, the Emotional Guidance GAN (EG-GAN) with EM Distance and Gradient Penalty, as well as classification strategies, is proposed to generate new fonts with combined multiple styles that infer by an expression recognition module. The results of the evaluation experiments and the resolution of the synthesized font characters show the credibility of our model.

Hashing technique has been extensively utilized in approximate nearest neighbor (ANN) search for large-scale image retrieval by virtue of its storage simplicity and computational efficiency. Recently, many researches show that hashing methods based. on deep neural networks (DNNs) can improve retrieval accuracy by simultaneously learning both deep feature representation and hashing functions in an end-to-end framework. Most deep supervised hashing methods aim to preserve the distance or similarity between data points using the similarity relationships constructed based on semantic labels of images, while ignoring the classification ability of the generated hash codes. However, the semantic labels themselves carry more information than the corresponding similarity labels. We propose an Improved. Pairwise-based Deep Hashing (IPDH) method to generate hash codes with powerful classification ability by exploring the global distribution of semantic labels. Specifically, the proposed IPDH method aims to minimize the information loss generated during the process of classification prediction to ensure that the output predicted labels of the network model has a similar distribution with those from the original semantic labels. Comprehensive experiments show that the proposed IPDH method can obtain better improvement than other state-of-the-art algorithms.