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

We address the problem of grounding free-form textual phrases by using weaksupervision from image-caption pairs. We propose a novel end-to-end model thatuses caption-to-image retrieval as a `downstream' task to guide the process ofphrase localization. Our method, as a first step, infers the latentcorrespondences between regions-of-interest (RoIs) and phrases in the captionand creates a discriminative image representation using these matched RoIs. Ina subsequent step, this (learned) representation is aligned with the caption.Our key contribution lies in building this `caption-conditioned' image encodingwhich tightly couples both the tasks and allows the weak supervision toeffectively guide visual grounding. We provide an extensive empirical andqualitative analysis to investigate the different components of our proposedmodel and compare it with competitive baselines. For phrase localization, wereport an improvement of 4.9% (absolute) over the prior state-of-the-art on theVisualGenome dataset. We also report results that are at par with thestate-of-the-art on the downstream caption-to-image retrieval task on COCO andFlickr30k datasets.

Align2Ground: Weakly Supervised Phrase Grounding Guided by Image-Caption Alignment