Fashion analysis is an attractive domain for vision research due to its direct applications in e-commerce contexts. However, fashion datasets are commonly rather demanding, as both objects and attributes tend to be fine-grained and thus result in very long-tailed datasets. Furthermore, relationships between objects and attributes are often dense, but are crucial for the performance of fashion applications. In this paper, we propose to generate scene graphs for existing fashion datasets. By detecting relationships between fashion objects, their parts, and their attributes we gain a better understanding of the scenes. As no current fashion dataset provides scene graphs, we generate relationships between fashion objects from existing annotations. The output is post-processed and filtered to generate a meaningful scene graph for each image. In the experiments we can show existing applications like image retrieval benefiting from the scene graph understanding. We first evaluate the accuracy of the generated scene graphs. Then, we employ scene graphs to fashion image retrieval in order to showcase their performance in real applications. The results show various benefits for fashion applications by exploiting scene graph knowledge. The sources and model for the proposed method will be made available after publication.

A smile is a representative expression of happiness or high quality-of-life; however, automatic recognition of a smile according to happiness remains a challenging task. Because expressions of happiness are strongly dependent upon physical condition and occurrence of other emotions, and similar facial expression often occur under different emotions, we consider that there is no absolute visual pattern of a smile corresponding to happiness. Therefore, in this study, we assumed that a ``smile with happiness'' is observed as the temporal ascent in the degree of smiling and attempted to recognize this by capturing changes in facial expression within temporally sequential images. As an implementation of this scheme, we proposed a Siamese-structure deep neural network to compare facial expressions in two input images and estimate the existence of smile ascension or descension. For primal analysis of the proposed network, we developed a unique smiling dataset containing image pairs with various changes in smiling degree, including slight changes. The results demonstrated that the proposed method achieved nearly perfect recognition with >0.95 accuracy when recognizing changes in the degree of smiling that humans certainly recognize. Attention regions that contributed to the predicted labels were concentrated on the mouth, cheeks, and tail of the eyes, which indicates a reasonable function for recognizing changes in smiling degree was constructed by the proposed method.