Table 4 We investigate the source of improvement with Deformable Attention in LocATe, compared to Graph Attention (GAT)

From: Localization and recognition of human action in 3D using transformers

Method

Rec. loss ↓

Loc. loss ↓

mAP ↑

LocATe w/ GAT

0.671

0.395

23.4

LocATe

0.362

0.374

36.0

  1. We break down the sources of error into two components: recognition loss (column 2) and localization loss (column 3). It is essential to recognize that lower loss values indicate superior performance. We gauge performance using the metric mAP@tIoU = 0.5, where higher values indicate better performance. mAP mean Average Precision, tIoU threshold IoU, Rec recognition, Loc localization.
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