English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In this paper, we focus on triplet-based deep binary embedding networks forimage retrieval task. The triplet loss has been shown to be most effective forthe ranking problem. However, most of the previous works treat the tripletsequally or select the hard triplets based on the loss. Such strategies do notconsider the order relations, which is important for retrieval task. To thisend, we propose an order-aware reweighting method to effectively train thetriplet-based deep networks, which up-weights the important triplets anddown-weights the uninformative triplets. First, we present the order-awareweighting factors to indicate the importance of the triplets, which depend onthe rank order of binary codes. Then, we reshape the triplet loss to thesquared triplet loss such that the loss function will put more weights on theimportant triplets. Extensive evaluations on four benchmark datasets show thatthe proposed method achieves significant performance compared with thestate-of-the-art baselines.

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