FashionSearch++: Improving Consumer-to-Shop Clothes Retrieval with Hard Negatives
Abstract: Consumer-to-shop clothes retrieval has recently emerged in computer vision and multimedia communities with the development of architectures that can find similar in-shop clothing images given a query photo. Due to its nature, the main challenge lies in the domain gap between user-acquired and in-shop images. In this paper, we follow the most recent successful research in this area employing convolutional neural networks as feature extractors and propose to enhance the training supervision through a modified triplet loss that takes into account hard negative examples. We test the proposed approach on the Street2Shop dataset, achieving results comparable to state-of-the-art solutions and demonstrating good generalization properties when dealing with different settings and clothing categories.
Citation:
Morelli, Davide; Cornia, Marcella; Cucchiara, Rita "FashionSearch++: Improving Consumer-to-Shop Clothes Retrieval with Hard Negatives" Proceedings of the 11th Italian Information Retrieval Workshop, IIR 2021, vol. 2947, Bari, Italy, September 13-15, 2021, 2021
not available
Paper download:
- Author version: