Learning Embeddings for Product Visual Search with Triplet Loss and Online Sampling
This addresses content-based image retrieval for e-commerce applications, representing an incremental improvement over existing methods.
The paper tackles product visual search by learning an embedding function using triplet loss and online sampling, achieving state-of-the-art performance on the DeepFashion dataset and competitive results on the Stanford Online Products dataset.
In this paper, we propose learning an embedding function for content-based image retrieval within the e-commerce domain using the triplet loss and an online sampling method that constructs triplets from within a minibatch. We compare our method to several strong baselines as well as recent works on the DeepFashion and Stanford Online Product datasets. Our approach significantly outperforms the state-of-the-art on the DeepFashion dataset. With a modification to favor sampling minibatches from a single product category, the same approach demonstrates competitive results when compared to the state-of-the-art for the Stanford Online Products dataset.