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

Most image captioning frameworks generate captions directly from images,learning a mapping from visual features to natural language. However, editingexisting captions can be easier than generating new ones from scratch.Intuitively, when editing captions, a model is not required to learninformation that is already present in the caption (i.e. sentence structure),enabling it to focus on fixing details (e.g. replacing repetitive words). Thispaper proposes a novel approach to image captioning based on iterative adaptiverefinement of an existing caption. Specifically, our caption-editing modelconsisting of two sub-modules: (1) EditNet, a language module with an adaptivecopy mechanism (Copy-LSTM) and a Selective Copy Memory Attention mechanism(SCMA), and (2) DCNet, an LSTM-based denoising auto-encoder. These componentsenable our model to directly copy from and modify existing captions.Experiments demonstrate that our new approach achieves state-of-art performanceon the MS COCO dataset both with and without sequence-level training.

Show, Edit and Tell: A Framework for Editing Image Captions