Boundary-aware Backward-Compatible Representation via Adversarial Learning in Image Retrieval
This addresses a practical deployment bottleneck for large-scale image retrieval systems, offering an incremental improvement over existing backward-compatible training techniques.
The paper tackles the problem of backward-compatible training in image retrieval, where new models must be deployable without re-computing old embeddings, and introduces AdvBCT, an adversarial method with elastic boundary constraints that outperforms other methods on compatibility and discrimination across datasets like GLDv2, ROxford, and RParis.
Image retrieval plays an important role in the Internet world. Usually, the core parts of mainstream visual retrieval systems include an online service of the embedding model and a large-scale vector database. For traditional model upgrades, the old model will not be replaced by the new one until the embeddings of all the images in the database are re-computed by the new model, which takes days or weeks for a large amount of data. Recently, backward-compatible training (BCT) enables the new model to be immediately deployed online by making the new embeddings directly comparable to the old ones. For BCT, improving the compatibility of two models with less negative impact on retrieval performance is the key challenge. In this paper, we introduce AdvBCT, an Adversarial Backward-Compatible Training method with an elastic boundary constraint that takes both compatibility and discrimination into consideration. We first employ adversarial learning to minimize the distribution disparity between embeddings of the new model and the old model. Meanwhile, we add an elastic boundary constraint during training to improve compatibility and discrimination efficiently. Extensive experiments on GLDv2, Revisited Oxford (ROxford), and Revisited Paris (RParis) demonstrate that our method outperforms other BCT methods on both compatibility and discrimination. The implementation of AdvBCT will be publicly available at https://github.com/Ashespt/AdvBCT.