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Action quality assessment is crucial in areas of sports, surgery and assembly line where action skills can be evaluated. In this paper, we propose the Segment-based P3D-fused network S3D built-upon ED-TCN and push the performance on the UNLV-Dive dataset by a significant margin. We verify that segment-aware training performs better than full-video training which turns out to focus on the water spray. We show that temporal segmentation can be embedded with few efforts.

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Video enhancement methods enable to optimize the viewing of video content at the end-user side. Most approaches do not consider the compressed nature of the available content. In the present work, we build upon a recently proposed video enhancement approach that explicitly models a compression stage. To apply the enhancement framework on compressed representations requires to extract specific syntax elements during their decoding. This additional information embeds the enhanced result in a domain that closely fits the observation.

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