Cross-domain action recognition is a less explored field of research until recently. The previous approaches usually feed the pre-extracted video-level or segment-level feature vectors to shallow networks for regenerating them. These approaches cannot directly affect the full capability of the action recognition models. Considering the recent researches, CNN has an inductive bias towards the texture of images. In a domain-changing situations, this information is affected and changed easily. Moreover theses low-level information is mainly encoded in intermediate features and is also needed to be aligned. For exploring the effect of the adaptation between various levels of spatial dimensions of the feature map, we divided the model into several parts and performed adaptation for each step. However, not every stage play the important role in action recognition in the temporal axis. To more sensitive adaptation, we propose a similarity-based weighting strategy. We first calculate the discrimination loss for each stage. Next, these discrimination losses are weighted by their similarity. The discrimination losses become large if the source and target sample's similarity values are small in a certain stage. The proposed method achieves state-of-the-art performance on UCF101-HMDB full dataset.
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J. Kim et. al, "Trends in Lightweight Kernel for Many core Based High-Performance Computing", Electronics and Telecommunications Trends. Vol. 32, No. 4, 2017, KOGL Type 4: Source Indication + Commercial Use Prohibition + Change Prohibition
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