Smooth-AP: Smoothing the Path Towards Large-Scale Image Retrieval

• URL: http://arxiv.org/abs/2007.12163v2
• Date: Tue, 8 Sep 2020 18:02:12 GMT
• Title: Smooth-AP: Smoothing the Path Towards Large-Scale Image Retrieval
• Authors: Andrew Brown, Weidi Xie, Vicky Kalogeiton, Andrew Zisserman
• Abstract summary: Smooth-AP is a plug-and-play objective function that allows for end-to-end training of deep networks. We apply Smooth-AP to standard retrieval benchmarks: Stanford Online products and VehicleID. We also evaluate on larger-scale datasets: INaturalist for fine-grained category retrieval, VGGFace2 and IJB-C for face retrieval.
• Score: 94.73459295405507
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Optimising a ranking-based metric, such as Average Precision (AP), is notoriously challenging due to the fact that it is non-differentiable, and hence cannot be optimised directly using gradient-descent methods. To this end, we introduce an objective that optimises instead a smoothed approximation of AP, coined Smooth-AP. Smooth-AP is a plug-and-play objective function that allows for end-to-end training of deep networks with a simple and elegant implementation. We also present an analysis for why directly optimising the ranking based metric of AP offers benefits over other deep metric learning losses. We apply Smooth-AP to standard retrieval benchmarks: Stanford Online products and VehicleID, and also evaluate on larger-scale datasets: INaturalist for fine-grained category retrieval, and VGGFace2 and IJB-C for face retrieval. In all cases, we improve the performance over the state-of-the-art, especially for larger-scale datasets, thus demonstrating the effectiveness and scalability of Smooth-AP to real-world scenarios.

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