Papers › Automatic Spatially-aware Fashion Concept Discovery
Automatic Spatially-aware Fashion Concept Discovery
Xintong Han, Zuxuan Wu, Phoenix X. Huang, Xiao Zhang, Menglong Zhu, Yuan Li, Yang Zhao, Larry S. Davis
This paper proposes an automatic spatially-aware concept discovery approach using weakly labeled image-text data from shopping websites. We first fine-tune GoogleNet by jointly modeling clothing images and their corresponding descriptions in a visual-semantic embedding space. Then, for each attribute (word), we generate its spatially-aware representation by combining its semantic word vector representation with its spatial representation derived from the convolutional maps of the fine-tuned network. The resulting spatially-aware representations are further used to cluster attributes into multiple groups to form spatially-aware concepts (e.g., the neckline concept might consist of attributes like v-neck, round-neck, etc). Finally, we decompose the visual-semantic embedding space into multiple concept-specific subspaces, which facilitates structured browsing and attribute-feedback product retrieval by exploiting multimodal linguistic regularities. We conducted extensive experiments on our newly collected Fashion200K dataset, and results on clustering quality evaluation and attribute-feedback product retrieval task demonstrate the effectiveness of our automatically discovered spatially-aware concepts.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Retrieval with Multi-Modal Query | Fashion200k | FashionConcept | Recall@1 | 6.3 | #8 of 8 | Archive leaderboard | report |
| Image Retrieval with Multi-Modal Query | Fashion200k | FashionConcept | Recall@10 | 19.9 | #8 of 8 | Archive leaderboard | report |
| Image Retrieval with Multi-Modal Query | Fashion200k | FashionConcept | Recall@50 | 38.3 | #8 of 8 | Archive leaderboard | report |
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