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

Due to the problem of performance constraints of unsupervised video objectdetection, its large-scale application is limited. In response to this painpoint, we propose another excellent method to solve this problematic point. Byincorporating motion characterization in unsupervised video object detection,detection accuracy is improved while reducing the computational amount of thenetwork. The whole network structure consists of dual-stream network, motionguidance module, and multi-scale progressive fusion module. The appearance andmotion representations of the detection target are obtained through adual-stream network. Then, the semantic features of the motion representationare obtained through the local attention mechanism in the motion guidancemodule to obtain the high-level semantic features of the appearancerepresentation. The multi-scale progressive fusion module then fuses thefeatures of different deep semantic features in the dual-stream network furtherto improve the detection effect of the overall network. We have conductednumerous experiments on the three datasets of DAVIS 16, FBMS, and ViSal. Theverification results show that the proposed method achieves superior accuracyand performance and proves the superiority and robustness of the algorithm.

Efficient Unsupervised Video Object Segmentation Network Based on Motion Guidance