%20and%20graph%20convolutional%20networks%20(GCN)%20as%20a%20processing%20module%20has%20shown%20promising%20results%20in%20skeleton-based%20video%20anomaly%20detection%20(SVAD).%20However%2C%20to%20maintain%20a%20lightweight%20model%20with%20low%20computational%20and%20storage%20complexity%2C%20shallow%20GCN%20and%20TCN%20blocks%20are%20constrained%20by%20small%20receptive%20fields%20and%20a%20lack%20of%20cross-dimension%20interaction%20capture.%20To%20tackle%20this%20limitation%2C%20we%20propose%20a%20lightweight%20module%20called%20the%20Dual%20Attention%20Module%20(DAM)%20for%20capturing%20cross-dimension%20interaction%20relationships%20in%20spatio-temporal%20skeletal%20data.%20It%20employs%20the%20frame%20attention%20mechanism%20to%20identify%20the%20most%20significant%20frames%20and%20the%20skeleton%20attention%20mechanism%20to%20capture%20broader%20relationships%20across%20fixed%20partitions%20with%20minimal%20parameters%20and%20flops.%20Furthermore%2C%20the%20proposed%20Dual%20Attention%20Normalizing%20Flow%20(DA-Flow)%20integrates%20the%20DAM%20as%20a%20post-processing%20unit%20after%20GCN%20within%20the%20normalizing%20flow%20framework.%20Simulations%20show%20that%20the%20proposed%20model%20is%20robust%20against%20noise%20and%20negative%20samples.%20Experimental%20results%20show%20that%20DA-Flow%20reaches%20competitive%20or%20better%20performance%20than%20the%20existing%20state-of-the-art%20(SOTA)%20methods%20in%20terms%20of%20the%20micro%20AUC%20metric%20with%20the%20fewest%20number%20of%20parameters.%20Moreover%2C%20we%20found%20that%20even%20without%20training%2C%20simply%20using%20random%20projection%20without%20dimensionality%20reduction%20on%20skeleton%20data%20enables%20substantial%20anomaly%20detection%20capabilities.)
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