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

Many state-of-the-art trackers usually resort to the pretrained convolutionalneural network (CNN) model for correlation filtering, in which deep featurescould usually be redundant, noisy and less discriminative for some certaininstances, and the tracking performance might thus be affected. To handle thisproblem, we propose a novel approach, which takes both advantages of goodgeneralization of generative models and excellent discrimination ofdiscriminative models, for visual tracking. In particular, we learn compact,discriminative and target-oriented feature representations using the Laplaciancoding algorithm that exploits the dependence among the input local features ina discriminative correlation filter framework. The feature representations andthe correlation filter are jointly learnt to enhance to each other via a fastsolver which only has very slight computational burden on the tracking speed.Extensive experiments on three benchmark datasets demonstrate that thisproposed framework clearly outperforms baseline trackers with a modest impacton the frame rate, and performs comparably against the state-of-the-artmethods.

Learning Compact Target-Oriented Feature Representations for Visual Tracking