论文标题
使用人工智能分析时尚趋势
Using Artificial Intelligence to Analyze Fashion Trends
论文作者
论文摘要
分析时尚趋势在时尚界至关重要。当前的时尚预测公司（例如WGSN）利用来自世界各地的视觉信息来分析和预测时尚趋势。但是，分析时尚趋势是耗时的，并且需要劳动力大量，需要个人员工的手动编辑和分类。为了提高此类基于图像的信息的数据分析的效率并降低分析时尚图像的成本，本研究提出了使用人工智能（A.I.）算法进行数据驱动的定量抽象方法。具体而言，A.I.模型在不同方案的大规模数据集中对时尚图像进行了培训，例如在在线商店和街头快照中。该模型用于检测服装并分类服装属性，例如纹理，服装样式以及跑道照片和视频的详细信息。发现AI。模型可以生成对检测区域的丰富属性描述，并准确地绑定图像中的服装。采用A.I.算法表现出有希望的结果，并且有可能自动对服装类型和细节进行分类，这可以使趋势预测的过程更具成本效益和更快。
Analyzing fashion trends is essential in the fashion industry. Current fashion forecasting firms, such as WGSN, utilize the visual information from around the world to analyze and predict fashion trends. However, analyzing fashion trends is time-consuming and extremely labor intensive, requiring individual employees' manual editing and classification. To improve the efficiency of data analysis of such image-based information and lower the cost of analyzing fashion images, this study proposes a data-driven quantitative abstracting approach using an artificial intelligence (A.I.) algorithm. Specifically, an A.I. model was trained on fashion images from a large-scale dataset under different scenarios, for example in online stores and street snapshots. This model was used to detect garments and classify clothing attributes such as textures, garment style, and details for runway photos and videos. It was found that the A.I. model can generate rich attribute descriptions of detected regions and accurately bind the garments in the images. Adoption of A.I. algorithm demonstrated promising results and the potential to classify garment types and details automatically, which can make the process of trend forecasting more cost-effective and faster.
