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Fashion recommendation, aiming to explore specific user preference in fashion, has become an important research topic for its practical significance to the fashion business sector. However, little work has been done on an important sub-task called sequential fashion recommendation, which aims to capture additional short-term fashion interest of users by modeling the item-to-item transitions. In this paper, we propose a novel Attentional Content-level Translation-based Recommender (ACTR) framework, which simultaneously models the instant user intent of each transition and the intent-specific transition probability. Specifically, we define instant intent with the relationships between adjacent items that the users interacted, which are the three fundamental domain-specific relationships of: match, substitute and others. To further exploit the characteristics of fashion domain and alleviate the item transition sparsity problem, we augment the item-level transition modeling with multiple sub-transitions using various content-level attributes. An attention mechanism is further devised to effectively aggregate multiple content-level transitions. To the best of our knowledge, this is the first work that specifies the implicit user actions in fashion shopping with explicit instant intent and through which to enhance the connectivity of fashion items to boost the recommendation performance. Extensive experiments on two real-world fashion E-commerce datasets demonstrate the effectiveness of the proposed method in sequential fashion recommendation task.

Current fashion image searching technology based on fine-grained fashion recognition on fashion images has recently achieved great success in online shopping. However, this technique is limited to a single domain—real product images—and thus is inflexible. Recognition and search performance are degraded to a large extent when the distribution of the target data is different from the source training data. To improve the flexibility of fashion image retrieval, we propose multi-domain fashion image recognition in this work. We firstly established Fashion-DA, a large-scale fashion dataset comprising 14 fashion categories and a total of 13,435 images originating from three domains. Then, we propose an unsupervised domain adaption approach based on adaptive feature norm to handle data with different feature distributions. The experiment evaluated the effectiveness of the proposed method.

Fabric defect detection plays an increasingly important role in the industrial automation application for fabric production, but how to detect defects rapidly and accurately is still challenging. In this study, we propose a powerful fabric defect detection method using a hybrid of convolutional neural network (CNN) and variational autoencoder (VAE). The convolutional layers are used for extracting fabric image pattern features and the variational autoencoder is used for modeling the latent characteristics and inferring a reconstruction. The defect positions can be detected by the differences between the original image and the reconstruction image. The proposed method is validated on public patterned fabric datasets. The experimental results demonstrate that the proposed model can achieve outstanding performance in both image level and pixel level defect detection.

We present a multi-task model to perform multiple fashion attribute recognition and retrieval together. Although classical computer vision problems, they still lack good performance in multiple classes. In the fashion domain, the recognition task to capture the attributes of fashion clothing is always challenging, as well as the retrieval task to find similar items of fashion clothing. To handle the first challenge, we built a recognition model to parallelly and independently output a set of labels, each for a pre-defined single attribute notion. To handle the second challenge, we embedded the fashion clothing image into a feature vector, where different subspaces capture different notions of similarities. These two sub-tasks were then connected to be trained jointly in a multi-task learning framework. Their losses were also well aligned, combined, and optimized collectively.