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

Learning the hash representation of multi-view heterogeneous data is animportant task in multimedia retrieval. However, existing methods fail toeffectively fuse the multi-view features and utilize the metric informationprovided by the dissimilar samples, leading to limited retrieval precision.Current methods utilize weighted sum or concatenation to fuse the multi-viewfeatures. We argue that these fusion methods cannot capture the interactionamong different views. Furthermore, these methods ignored the informationprovided by the dissimilar samples. We propose a novel deep metric multi-viewhashing (DMMVH) method to address the mentioned problems. Extensive empiricalevidence is presented to show that gate-based fusion is better than typicalmethods. We introduce deep metric learning to the multi-view hashing problems,which can utilize metric information of dissimilar samples. On theMIR-Flickr25K, MS COCO, and NUS-WIDE, our method outperforms the currentstate-of-the-art methods by a large margin (up to 15.28 mean Average Precision(mAP) improvement).

Deep Metric Multi-View Hashing for Multimedia Retrieval