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

Local features at neighboring spatial positions in feature maps have highcorrelation since their receptive fields are often overlapped. Self-attentionusually uses the weighted sum (or other functions) with internal elements ofeach local feature to obtain its weight score, which ignores interactions amonglocal features. To address this, we propose an effective interaction-awareself-attention model inspired by PCA to learn attention maps. Furthermore,since different layers in a deep network capture feature maps of differentscales, we use these feature maps to construct a spatial pyramid and thenutilize multi-scale information to obtain more accurate attention scores, whichare used to weight the local features in all spatial positions of feature mapsto calculate attention maps. Moreover, our spatial pyramid attention isunrestricted to the number of its input feature maps so it is easily extendedto a spatio-temporal version. Finally, our model is embedded in general CNNs toform end-to-end attention networks for action classification. Experimentalresults show that our method achieves the state-of-the-art results on theUCF101, HMDB51 and untrimmed Charades.

Interaction-aware Spatio-temporal Pyramid Attention Networks for Action Classification