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

The CLIP model has been recently proven to be very effective for a variety ofcross-modal tasks, including the evaluation of captions generated fromvision-and-language architectures. In this paper, we propose a new recipe for acontrastive-based evaluation metric for image captioning, namelyPositive-Augmented Contrastive learning Score (PAC-S), that in a novel wayunifies the learning of a contrastive visual-semantic space with the additionof generated images and text on curated data. Experiments spanning severaldatasets demonstrate that our new metric achieves the highest correlation withhuman judgments on both images and videos, outperforming existingreference-based metrics like CIDEr and SPICE and reference-free metrics likeCLIP-Score. Finally, we test the system-level correlation of the proposedmetric when considering popular image captioning approaches, and assess theimpact of employing different cross-modal features. Our source code and trainedmodels are publicly available at: https://github.com/aimagelab/pacscore.

Positive-Augmented Contrastive Learning for Image and Video Captioning Evaluation