Recently, outfit compatibilitymodeling, which aims to evaluate the compatibility of a given outfit that comprises a set of fashion items, has gained growing research attention. Although existing studies have achieved...
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ISBN:
(纸本)9781450386517
Recently, outfit compatibilitymodeling, which aims to evaluate the compatibility of a given outfit that comprises a set of fashion items, has gained growing research attention. Although existing studies have achieved prominent progress, most of them overlook the essential global outfit representation learning, and the hidden complementary factors behind the outfit compatibility uncovering. Towards this end, we propose an Outfit compatibilitymodeling scheme via complementary Factorization, termed as OCM-CF. In particular, OCM-CF consists of two key components: context-aware outfit representation modeling and hidden complementary factors modeling. The former works on adaptively learning the global outfit representation with graph convolutional networks and the multihead attention mechanism, where the item context is fully explored. The latter targets at uncovering the latent complementary factors with multiple parallel networks, each of which corresponds to a factor-oriented context-aware outfit representation modeling. In this part, a new orthogonality-based complementarity regularization is proposed to encourage the learned factors to complement each other and better characterize the outfit compatibility. Finally, the outfit compatibility is obtained by summing all the hidden complementary factor-oriented outfit compatibility scores, each of which is derived from the corresponding outfit representation. Extensive experiments on two real-world datasets demonstrate the superiority of our OCM-CF over the state-of-the-art methods.
Empowered by the continuous integration of social multimedia and artificial intelligence, the application scenarios of information retrieval (IR) progressively tend to be diversified and personalized. Currently, User-...
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Empowered by the continuous integration of social multimedia and artificial intelligence, the application scenarios of information retrieval (IR) progressively tend to be diversified and personalized. Currently, User-Generated Content (UGC) systems have great potential to handle the interactions between large-scale users and massive media contents. As an emerging multimedia IR, Fashion compatibilitymodeling (FCM) aims to predict the matching degree of each given outfit and provide complementary item recommendation for user queries. Although existing studies attempt to explore the FCM task from a multimodal perspective with promising progress, they still fail to fully leverage the interactions between multimodal information or ignore the item-item contextual connectivities of intra-outfit. In this paper, a novel fashion compatibilitymodeling scheme is proposed based on Correlation-aware Cross-modal Attention Network. To better tackle these issues, our work mainly focuses on enhancing comprehensive multimodal representations of fashion items by integrating the cross-modal collaborative contents and uncovering the contextual correlations. Since the multimodal information of fashion items can deliver various semantic clues from multiple aspects, a modality-driven collaborative learning module is presented to explicitly model the interactions of modal consistency and complementarity via a co-attention mechanism. Considering the rich connections among numerous items in each outfit as contextual cues, a correlation-aware information aggregation module is further designed to adaptively capture significant intra-correlations of item-item for characterizing the content-aware outfit representations. Experiments conducted on two real-world fashion datasets demonstrate the superiority of our approach over state-of-the-art methods.
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