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 investigate a novel problem of telling the differencebetween image pairs in natural language. Compared to previous approaches forsingle image captioning, it is challenging to fetch linguistic representationfrom two independent visual information. To this end, we have proposed aneffective encoder-decoder caption framework based on Hyper Convolution Net. Inaddition, a series of novel feature fusing techniques for pairwise visualinformation fusing are introduced and a discriminating referee is proposed toevaluate the pipeline. Because of the lack of appropriate datasets to supportthis task, we have collected and annotated a large new dataset with AmazonMechanical Turk (AMT) for generating captions in a pairwise manner (with 14764images and 26710 image pairs in total). The dataset is the first one on therelative difference caption task that provides descriptions in free language.We evaluate the effectiveness of our model on two datasets in the field and itoutperforms the state-of-the-art approach by a large margin.

Tell-the-difference: Fine-grained Visual Descriptor via a Discriminating Referee