Clothing Recognition in the Wild using the Amazon Catalog
| Autor: | Michael Donoser, Bojan Pepik, Fabian Caba Heilbron, Zohar Barzelay |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Training set
Information retrieval Computer science business.industry Amazon rainforest 02 engineering and technology Clothing Influencer marketing Data modeling 0202 electrical engineering electronic engineering information engineering Task analysis 020201 artificial intelligence & image processing Artificial intelligence business |
| Zdroj: | ICCV Workshops |
| DOI: | 10.1109/iccvw.2019.00385 |
| Popis: | The emergence of online influencers, the explosion of video content, and the massive amount of movie collections have served as an advertising vehicle for the fashion industry. This trend has created the need for automated methods that recognize people's outfit in such image and video collections. However, existing computer vision solutions for fashion recognition require an enormous amount of labeled data for training, which is prohibitively expensive. In this work, we propose an approach to build clothing recognition models for real-world scenarios. Our approach exploits images from the Amazon Catalog as training data. By using the catalog data as an additional training source, we boost the recognition accuracy on the challenging real world images of the DeepFashion dataset achieving stateof-the-art performance. We introduce the first dataset for clothing recognition in movies. In this scenario, we find that the use of catalog data for training becomes even more crucial, as it provides an accuracy boost of 10%. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|