Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

6-2024

Abstract

Existing methods for asymmetric image retrieval employ a rigid pairwise similarity constraint between the query network and the larger gallery network. However, these oneto-one constraint approaches often fail to maintain retrieval order consistency, especially when the query network has limited representational capacity. To overcome this problem, we introduce the Decoupled Differential Distillation (D3still) framework. This framework shifts from absolute one-to-one supervision to optimizing the relational differences in pairwise similarities produced by the query and gallery networks, thereby preserving a consistent retrieval order across both networks. Our method involves computing a pairwise similarity differential matrix within the gallery domain, which is then decomposed into three components: feature representation knowledge, inconsistent pairwise similarity differential knowledge, and consistent pairwise similarity differential knowledge. This strategic decomposition aligns the retrieval ranking of the query network with the gallery network effectively. Extensive experiments on various benchmark datasets reveal that D3still surpasses state-of-the-art methods in asymmetric image retrieval.

Keywords

Asymmetric image retrieval, Decoupled differential distillation framework

Discipline

Artificial Intelligence and Robotics | Graphics and Human Computer Interfaces

Research Areas

Intelligent Systems and Optimization

Publication

Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2024) : Seattle, WA, USA, June 16-22

First Page

17181

Last Page

17190

Identifier

10.1109/CVPR52733.2024.01626

Publisher

IEEE

City or Country

Seattle, USA

Additional URL

https://doi.org/10.1109/CVPR52733.2024.01626

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