Mining Logic Patterns from Visual Data Logic reasoning, a fundamental ability of human intelligence, is an important topic in artificial intelligence. Often, these reasoning patterns need to beforehand be provided by some domain experts, which has resulted in an interesting research topic: can reasoning patterns be directly learned from given data in which meaning of any symbols is not known in advance? In order to study this issue, in this study, we design four visual logic data set (named FashionLogic) using the FashionMNIST data, in which first each image is embedded with n special figure symbols each of which is a picture from the FashionMNIST data set, then a relation (the relations: Bitwise And, Bitwise Or, Addition and Subtraction are used in this paper) is added to any two images to generate one output image. And these resulting images are called the logical images. Our aim is to obtain a learning model that can directly mine and reason the logic relation between two input images and one output image, without presetting any reasoning patterns (i.e. a learning model does not know the meaning of the figure symbols embedded in the images and the relation between the input images and the output image in advance). This novel task, mining the logic patterns from the FashionLogic data set, is called a fashion logic task in this paper. In this work, we test the performances of many typical neural network models on FashionLogic data set, and produce fruitful results which indicates that it is feasible to directly mine the logic reasoning patterns using a learning strategy from given data. Last, we test and find that the ability of models is very poor to mine the abstract logic patterns on the more complex data sets called FashionLogicV2.

Accuracy Improvement of Fashion Style Estimation with Attention Control of a Classifier Computer vision has begun to be applied to fashion applications owing to the remarkable development of image recognition technology and deep learning. Fashion style estimation, the focus of this paper, is a hot topic as it provides useful information and services to consumers. This paper aims to enhance the accuracy of fashion style estimation by improving the classifier. To control the attention of a classifier, two different approaches are followed: human detection after segmentation with the aim of improving on previous schemes and enhancing the visual attention model (VAM). Experiments were conducted using the Hipster Wars dataset to train a final SVMbased classifier and the WEARStyle dataset to construct a feature extractor. The results using SSDbased detection after PSPNetbased segmentation, VAM with ICNet, and VAM with PSPNet showed classification accuracies of 83.8%, 84.7%, and 85.1%, respectively. All these outperformed VAM with full convolutional network (FCN), which resulted in a classification accuracy of 83.0% under the same experimental conditions. The results indicate that classification accuracy can be improved upon when the attention of the classifier is controlled.