  Video domain generalization aims to learn generalizable video classification
models for unseen target domains by training in a source domain. A critical
challenge of video domain generalization is to defend against the heavy
reliance on domain-specific cues extracted from the source domain when
recognizing target videos. To this end, we propose to perceive diverse
spatial-temporal cues in videos, aiming to discover potential domain-invariant
cues in addition to domain-specific cues. We contribute a novel model named
Spatial-Temporal Diversification Network (STDN), which improves the diversity
from both space and time dimensions of video data. First, our STDN proposes to
discover various types of spatial cues within individual frames by spatial
grouping. Then, our STDN proposes to explicitly model spatial-temporal
dependencies between video contents at multiple space-time scales by
spatial-temporal relation modeling. Extensive experiments on three benchmarks
of different types demonstrate the effectiveness and versatility of our
approach.
