In the quest for robust hand segmentation methods, we evaluated the performance of the state of the art semantic segmentation methods, off the shelf and fine-tuned, on existing datasets.
Can performance on the task of action quality assessment (AQA) be improved by exploiting a description of the action and its quality?
Ranked #1 on Action Quality Assessment on MTL-AQA
In this work we propose a novel temporal pose-sequence modeling framework, which can embed the dynamics of 3D human-skeleton joints to a continuous latent space in an efficient manner.
We then combine adversarial training with multi-modal self-supervision, showing that our approach outperforms other UDA methods by 3%.
However, existing query-based reasoning methods have not considered handling of inter-dependent queries which is a unique requirement of semantic role prediction in SR.
The hallucination task is treated as an auxiliary task, which can be used with any other action related task in a multitask learning setting.