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

Self-supervised learning has become increasingly important to leverage theabundance of unlabeled data available on platforms like YouTube. Whereas mostexisting approaches learn low-level representations, we propose a jointvisual-linguistic model to learn high-level features without any explicitsupervision. In particular, inspired by its recent success in languagemodeling, we build upon the BERT model to learn bidirectional jointdistributions over sequences of visual and linguistic tokens, derived fromvector quantization of video data and off-the-shelf speech recognition outputs,respectively. We use VideoBERT in numerous tasks, including actionclassification and video captioning. We show that it can be applied directly toopen-vocabulary classification, and confirm that large amounts of training dataand cross-modal information are critical to performance. Furthermore, weoutperform the state-of-the-art on video captioning, and quantitative resultsverify that the model learns high-level semantic features.

VideoBERT: A Joint Model for Video and Language Representation Learning