Name
Abstract | ||
|---|---|---|
We present an approach to detect the main product in fashion images by exploiting the textual metadata associated with each image. Our approach is based on a Convolutional Neural Network and learns a joint embedding of object proposals and textual metadata to predict the main product in the image. We additionally use several complementary classification and overlap losses in order to improve training stability and performance. Our tests on a large-scale dataset taken from eight e-commerce sites show that our approach outperforms strong baselines and is able to accurately detect the main product in a wide diversity of challenging fashion images. |
Year | Venue | Field |
|---|---|---|
2017 | ICCV Workshops | Metadata,Embedding,Pattern recognition,Convolutional neural network,Computer science,Artificial intelligence,Deep learning,Modal |
DocType | Citations | PageRank |
Conference | 3 | 0.39 |
References | Authors | |
14 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| LongLong Yu | 1 | 3 | 1.41 |
| Edgar Simo-Serra | 2 | 646 | 27.31 |
| Francesc Moreno-Noguer | 3 | 1647 | 93.46 |
| Antonio Rubio | 4 | 6 | 1.92 |