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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

We present an architecture and a training recipe that adapts pre-trainedopen-world image models to localization in videos. Understanding the openvisual world (without being constrained by fixed label spaces) is crucial formany real-world vision tasks. Contrastive pre-training on large image-textdatasets has recently led to significant improvements for image-level tasks.For more structured tasks involving object localization applying pre-trainedmodels is more challenging. This is particularly true for video tasks, wheretask-specific data is limited. We show successful transfer of open-world modelsby building on the OWL-ViT open-vocabulary detection model and adapting it tovideo by adding a transformer decoder. The decoder propagates objectrepresentations recurrently through time by using the output tokens for oneframe as the object queries for the next. Our model is end-to-end trainable onvideo data and enjoys improved temporal consistency compared totracking-by-detection baselines, while retaining the open-world capabilities ofthe backbone detector. We evaluate our model on the challenging TAO-OWbenchmark and demonstrate that open-world capabilities, learned fromlarge-scale image-text pre-training, can be transferred successfully toopen-world localization across diverse videos.

Video OWL-ViT: Temporally-consistent open-world localization in video