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

The success of deep learning models has led to their adaptation and adoptionby prominent video understanding methods. The majority of these approachesencode features in a joint space-time modality for which the inner workings andlearned representations are difficult to visually interpret. We propose LEArnedPreconscious Synthesis (LEAPS), an architecture-independent method forsynthesizing videos from the internal spatiotemporal representations of models.Using a stimulus video and a target class, we prime a fixed space-time modeland iteratively optimize a video initialized with random noise. Additionalregularizers are used to improve the feature diversity of the synthesizedvideos alongside the cross-frame temporal coherence of motions. Wequantitatively and qualitatively evaluate the applicability of LEAPS byinverting a range of spatiotemporal convolutional and attention-basedarchitectures trained on Kinetics-400, which to the best of our knowledge hasnot been previously accomplished.

Leaping Into Memories: Space-Time Deep Feature Synthesis