Summary of Dif4ff: Leveraging Multimodal Diffusion Models and Graph Neural Networks For Accurate New Fashion Product Performance Forecasting, by Andrea Avogaro et al.
Dif4FF: Leveraging Multimodal Diffusion Models and Graph Neural Networks for Accurate New Fashion Product Performance Forecasting
by Andrea Avogaro, Luigi Capogrosso, Franco Fummi, Marco Cristani
First submitted to arxiv on: 7 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed model, Dif4FF, is a novel two-stage pipeline that leverages diffusion models conditioned on multimodal data to predict the sales of new fashion products. The pipeline first uses a multimodal score-based diffusion model to forecast multiple sales trajectories for various garments over time. These forecasts are then refined using a Graph Convolutional Network (GCN) architecture, which captures long-range dependencies within both temporal and spatial data. This approach achieves state-of-the-art results on the VISUELLE dataset, outperforming existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Dif4FF is a new way to predict how well new clothes will sell. Right now, it’s hard to make accurate predictions because we don’t have enough information about the products and trends are always changing. The current method used by fashion companies doesn’t work well when they’re trying to forecast sales of new items. This paper proposes a better approach that uses two stages: first, a special type of model called a diffusion model is used to make predictions, and then those predictions are refined using another type of model called a Graph Convolutional Network (GCN). This approach works well and achieves the best results so far. |
Keywords
» Artificial intelligence » Convolutional network » Diffusion » Diffusion model » Gcn
