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Existing video domain adaption (DA) methods need to store all temporalcombinations of video frames or pair the source and target videos, which arememory cost expensive and can't scale up to long videos. To address theselimitations, we propose a memory-efficient graph-based video DA approach asfollows. At first our method models each source or target video by a graph:nodes represent video frames and edges represent the temporal or visualsimilarity relationship between frames. We use a graph attention network tolearn the weight of individual frames and simultaneously align the source andtarget video into a domain-invariant graph feature space. Instead of storing alarge number of sub-videos, our method only constructs one graph with a graphattention mechanism for one video, reducing the memory cost substantially. Theextensive experiments show that, compared with the state-of-art methods, weachieved superior performance while reducing the memory cost significantly.

Memory Efficient Temporal & Visual Graph Model for Unsupervised Video Domain Adaptation