We propose an approach that jointly learns latent fashion concepts in visual-semantic space to measure compatibility between items.

The graph is constructed that consists of feature extraction layer, visual semantic embedding layer and outfit compatibility prediction layer. The compatibility ...

This work proposes an approach that jointly learns latent fashion concepts in visual-semantic space to measure compatibility between items and model a ...

We propose an approach that jointly learns latent fashion concepts in visual-semantic space to measure compatibility between items.

Bibliographic details on Learning Outfit Compatibility with Graph Attention Network and Visual-Semantic Embedding.

To learn the fashion compatibility and generate for the outfit, we propose an approach that jointly learns latent fashion concepts in visual-semantic space to ...

Jan 1, 2022 · For visual compatibility, we adopt the graph neural network to model the visual relationship between items. To generate an outfit that satisfies ...

Then, we learn vector representations of items by an attentive relational embedding propagation rule that performs messages aggregation and propagation along.

Dressing as a Whole: Outfit Compatibility Learning Based on Node-wise Graph Neural Networks ... Learning Type-Aware Embeddings for Fashion Compatibility ...

Oct 9, 2023 · We propose a partial VSE (PVSE) model, which enables fine-grained learning of each part of the fashion outfit.