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Deep Image-to-Video Adaptation and Fusion Networks for Action Recognition
For the same action, the knowledge learned from different media types, e. g., videos and images, may be related and complementary.
Transferable Feature Representation for Visible-to-Infrared Cross-Dataset Human Action Recognition
However, the infrared action data is limited until now, which degrades the performance of infrared action recognition.
Global Temporal Representation based CNNs for Infrared Action Recognition
Existing methods for infrared action recognition are either based on spatial or local temporal information, however, the global temporal information, which can better describe the movements of body parts across the whole video, is not considered.
Hierarchically Learned View-Invariant Representations for Cross-View Action Recognition
In order to make the feature representations of videos across views transferable, we then learn a transferable dictionary pair simultaneously from pairs of videos taken at different views to encourage each action video across views to have the same sparse representation.
