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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2021
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2101.08301 |
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| _version_ | 1866908898269593600 |
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| author | Khalid, Laila Gong, Wei |
| author_facet | Khalid, Laila Gong, Wei |
| contents | Fashion-focused artificial intelligence has rapidly advanced in recent years, driven by deep learning and its deployment in recommender systems, detection, retrieval, and analytics. Yet several consumer-facing domains remain comparatively under-surveyed despite their practical impact. This work provides a comprehensive review of methods, datasets, and evaluation metrics across four such domains: aesthetics, personalization, virtual try-on, and forecasting. We synthesize technical approaches spanning representation learning, preference modeling, image transformation, and time-series analysis; relate them to downstream recommender systems and user experience; and highlight cross-domain dependencies (e.g., aesthetics-informed personalization, trend-informed recommendations). We also catalog commonly used datasets and metrics, including those from object detection and image segmentation pipelines, where relevant to try-on and visual understanding. Finally, we identify open challenges and promising directions for integrated AI-driven fashion systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2101_08301 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Exploring AI in Fashion: A Review of Aesthetics, Personalization, Virtual Try-On, and Forecasting Khalid, Laila Gong, Wei Computer Vision and Pattern Recognition Machine Learning Fashion-focused artificial intelligence has rapidly advanced in recent years, driven by deep learning and its deployment in recommender systems, detection, retrieval, and analytics. Yet several consumer-facing domains remain comparatively under-surveyed despite their practical impact. This work provides a comprehensive review of methods, datasets, and evaluation metrics across four such domains: aesthetics, personalization, virtual try-on, and forecasting. We synthesize technical approaches spanning representation learning, preference modeling, image transformation, and time-series analysis; relate them to downstream recommender systems and user experience; and highlight cross-domain dependencies (e.g., aesthetics-informed personalization, trend-informed recommendations). We also catalog commonly used datasets and metrics, including those from object detection and image segmentation pipelines, where relevant to try-on and visual understanding. Finally, we identify open challenges and promising directions for integrated AI-driven fashion systems. |
| title | Exploring AI in Fashion: A Review of Aesthetics, Personalization, Virtual Try-On, and Forecasting |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2101.08301 |