A recent trend in urban computing involves utilizing multi-modal data for urban region embedding, which can be further expanded in a variety of downstream urban sensing tasks. Many previous studies rely on multi-graph embedding techniques and follow a two-stage paradigm: first building a k-nearest neighbor graph based on fixed region correlations for each view, and then blending multi-view information in a posterior stage to learn region representations. However, multi-graph construction and multi-graph representation learning are not associated in most existing two-stage studies, and the relationship between them is not leveraged, which can provide complementary information to each other. In this paper, we unify these two stages into one by constructing learnable weighted complete graphs of regions and propose a new one-stage Region Embedding method with Adaptive region correlation Discovery (READ). Specifically, READ comprises three modules, including a disentangled region feature learning module utilizing a city-context Transformer to encode regions' semantic and mobility features, and an adaptive weighted multi-graph construction module that builds multiple complete graphs with learnable weights based on disentangled features of regions. In addition, we propose a multi-graph representation learning module to yield effective region representations that integrate information from multiple graphs. We conduct thorough experiments on three downstream tasks to assess READ. Experimental results demonstrate that READ considerably outperforms state-of-the-art baseline methods in urban region embedding.

Recent years have witnessed a growing trend of fashion compatibility modeling, which scores the matching degree of the given outfit and then provides people with some dressing advice. Existing methods have primarily solved this problem by analyzing the discrete interaction among multiple complementary items. However, the fashion items would present certain occlusion and deformation when they are worn on the body. Therefore, the discrete item interaction cannot capture the fashion compatibility in a combined manner due to the neglect of a crucial factor: the overall try-on appearance. In light of this, we propose a multi-modal try-on-guided compatibility modeling scheme to jointly characterize the discrete interaction and try-on appearance of the outfit. In particular, we first propose a multi-modal try-on template generator to automatically generate a try-on template from the visual and textual information of the outfit, depicting the overall look of its composing fashion items. Then, we introduce a new compatibility modeling scheme which integrates the outfit try-on appearance into the traditional discrete item interaction modeling. To fulfill the proposal, we construct a large-scale real-world dataset from SSENSE, named FOTOS, consisting of 11,000 well-matched outfits and their corresponding realistic try-on images. Extensive experiments have demonstrated its superiority to state-of-the-arts.