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Found 3 papers, 1 papers with code
O-TALC: Steps Towards Combating Oversegmentation within Online Action Segmentation
In order to facilitate online action segmentation on a stream of incoming video data, we introduce two methods for improved training and inference of backbone action recognition models, allowing them to be deployed directly for online frame level classification.
Hand Guided High Resolution Feature Enhancement for Fine-Grained Atomic Action Segmentation within Complex Human Assemblies
Due to the rapid temporal and fine-grained nature of complex human assembly atomic actions, traditional action segmentation approaches requiring the spatial (and often temporal) down sampling of video frames often loose vital fine-grained spatial and temporal information required for accurate classification within the manufacturing domain.
Are there any 'object detectors' in the hidden layers of CNNs trained to identify objects or scenes?
We find that the different measures provide different estimates of object selectivity, with precision and CCMAS measures providing misleadingly high estimates.
