Classification is a Strong Baseline for Deep Metric Learning
This work addresses the efficiency and scalability of metric learning for image retrieval applications, though it is incremental as it adapts existing classification methods to new tasks.
The paper tackled the problem of deep metric learning for image retrieval by evaluating classification-based training on standard datasets, showing it is competitive with triplet-based methods across different feature dimensions and networks.
Deep metric learning aims to learn a function mapping image pixels to embedding feature vectors that model the similarity between images. Two major applications of metric learning are content-based image retrieval and face verification. For the retrieval tasks, the majority of current state-of-the-art (SOTA) approaches are triplet-based non-parametric training. For the face verification tasks, however, recent SOTA approaches have adopted classification-based parametric training. In this paper, we look into the effectiveness of classification based approaches on image retrieval datasets. We evaluate on several standard retrieval datasets such as CAR-196, CUB-200-2011, Stanford Online Product, and In-Shop datasets for image retrieval and clustering, and establish that our classification-based approach is competitive across different feature dimensions and base feature networks. We further provide insights into the performance effects of subsampling classes for scalable classification-based training, and the effects of binarization, enabling efficient storage and computation for practical applications.