8 papers with code • 0 benchmarks • 1 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
Moreover, we present a human expert baseline for the problem, as well as an extensive empirical study of various domain transfer methods and of what is detected by the pain recognition method trained on acute pain in the orthopedic dataset.
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.
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.
Extensive experiments on four standard few-shot action benchmarks show that our method clearly outperforms previous state-of-the-art methods, with the improvement particularly significant (10+\%) on the most challenging fine-grained action recognition benchmark.
The hallucination task is treated as an auxiliary task, which can be used with any other action related task in a multitask learning setting.
Ranked #1 on Scene Recognition on YUP++