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

Understanding images without explicit supervision has become an importantproblem in computer vision. In this paper, we address image captioning bygenerating language descriptions of scenes without learning from annotatedpairs of images and their captions. The core component of our approach is ashared latent space that is structured by visual concepts. In this space, thetwo modalities should be indistinguishable. A language model is first trainedto encode sentences into semantically structured embeddings. Image featuresthat are translated into this embedding space can be decoded into descriptionsthrough the same language model, similarly to sentence embeddings. Thistranslation is learned from weakly paired images and text using a loss robustto noisy assignments and a conditional adversarial component. Our approachallows to exploit large text corpora outside the annotated distributions ofimage/caption data. Our experiments show that the proposed domain alignmentlearns a semantically meaningful representation which outperforms previouswork.

Towards Unsupervised Image Captioning with Shared Multimodal Embeddings