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

How can unlabeled video augment visual learning? Existing methods perform"slow" feature analysis, encouraging the representations of temporally closeframes to exhibit only small differences. While this standard approach capturesthe fact that high-level visual signals change slowly over time, it fails tocapture *how* the visual content changes. We propose to generalize slow featureanalysis to "steady" feature analysis. The key idea is to impose a prior thathigher order derivatives in the learned feature space must be small. To thisend, we train a convolutional neural network with a regularizer on tuples ofsequential frames from unlabeled video. It encourages feature changes over timeto be smooth, i.e., similar to the most recent changes. Using five diversedatasets, including unlabeled YouTube and KITTI videos, we demonstrate ourmethod's impact on object, scene, and action recognition tasks. We further showthat our features learned from unlabeled video can even surpass a standardheavily supervised pretraining approach.

Slow and steady feature analysis: higher order temporal coherence in video