  Existing video domain adaption (DA) methods need to store all temporal
combinations of video frames or pair the source and target videos, which are
memory cost expensive and can't scale up to long videos. To address these
limitations, we propose a memory-efficient graph-based video DA approach as
follows. At first our method models each source or target video by a graph:
nodes represent video frames and edges represent the temporal or visual
similarity relationship between frames. We use a graph attention network to
learn the weight of individual frames and simultaneously align the source and
target video into a domain-invariant graph feature space. Instead of storing a
large number of sub-videos, our method only constructs one graph with a graph
attention mechanism for one video, reducing the memory cost substantially. The
extensive experiments show that, compared with the state-of-art methods, we
achieved superior performance while reducing the memory cost significantly.
