Paper#1 - POG: Personalized Outfit Generation for Fashion Recommendation at Alibaba iFashion
Key Notes
- Personalized Out€fit Generation (POG) model = user preferences
- regarding individual items + outfi€ts with Transformer architecture
- Personalization represents how the recommendations meet users’ personal fashion tastes
- Key features - brand, category, style, pattern
- Fashion Outfit Model (FOM) by learning the compatibilities between each item and all the other items within the outfit
- Personalized Outfit Generation (POG) model, which can generate compatible and personalized outfits based on users’ recent behaviors
- Combination of NLP, Vision, Graph Embedding
- This could be a combination of user-user, item-item and bought sequences of complete pairs
Paper #2 - MMFashion: An Open-Source Toolbox for Visual Fashion Analysis
Key Notes
- Fashion Attribute Prediction, Fashion Recognition and Retrieval, Fashion Landmark Detection, Fashion Parsing and Segmentation and Fashion Compatibility and Recommendation.
- Dataset - DeepFashion, Polyvore
- Clothes Retrieval
- Landmark Detection
- Cloth Detection and Segmentation
- Fashion Compatibility and Recommendation
Paper#3 - c+GAN: Complementary Fashion Item Recommendation
Key Notes
- Bidirectional LSTM model to sequentially predict the next item conditioned on previous ones
- Clustering the intensity field of the images, with K-means clustering results in these dominant clusters
- Combination of Text + Vision Similarity + GAN would be good
More Reads
CRAFT: Complementary Recommendation by Adversarial Feature Transform
Keep Reading. This is just very basic skimming!!!!
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