Improving Deep Binary Embedding Networks by Order-aware Reweighting of Triplets

TL;DR

This paper introduces an order-aware reweighting approach for triplet-based deep binary embedding networks, improving image retrieval by emphasizing important triplets based on their rank order.

Contribution

It proposes a novel order-aware reweighting method that enhances triplet training by considering the importance of triplets according to their rank order, leading to better retrieval performance.

Findings

Significant performance improvements on four benchmark datasets

Effective weighting of triplets based on rank order

Outperforms state-of-the-art baselines

Abstract

In this paper, we focus on triplet-based deep binary embedding networks for image retrieval task. The triplet loss has been shown to be most effective for the ranking problem. However, most of the previous works treat the triplets equally or select the hard triplets based on the loss. Such strategies do not consider the order relations, which is important for retrieval task. To this end, we propose an order-aware reweighting method to effectively train the triplet-based deep networks, which up-weights the important triplets and down-weights the uninformative triplets. First, we present the order-aware weighting factors to indicate the importance of the triplets, which depend on the rank order of binary codes. Then, we reshape the triplet loss to the squared triplet loss such that the loss function will put more weights on the important triplets. Extensive evaluations on four benchmark datasets show that the proposed method achieves significant performance compared with the state-of-the-art baselines.