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PAT: Position-Aware Transformer for Dense Multi-Label Action Detection
To address this issue, we (i) embed relative positional encoding in the self-attention mechanism and (ii) exploit multi-scale temporal relationships by designing a novel non hierarchical network, in contrast to the recent transformer-based approaches that use a hierarchical structure.
Ranked #1 on
Action Detection
on MultiTHUMOS
Unsupervised View-Invariant Human Posture Representation
Most recent view-invariant action recognition and performance assessment approaches rely on a large amount of annotated 3D skeleton data to extract view-invariant features.
VI-Net: View-Invariant Quality of Human Movement Assessment
We propose a view-invariant method towards the assessment of the quality of human movements which does not rely on skeleton data.
