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Found 3 papers, 1 papers with code
Hierarchical Graph Convolutional Skeleton Transformer for Action Recognition
Graph convolutional networks (GCNs) have emerged as dominant methods for skeleton-based action recognition.
Rethinking the Misalignment Problem in Dense Object Detection
On the basis of SALT and SDR loss, we propose SALT-Net, which explicitly exploits task-aligned point-set features for accurate detection results.
Objects as Extreme Points
We also propose a new metric to measure the similarity between two groups of extreme points, namely, Extreme Intersection over Union (EIoU), and incorporate this EIoU as a new regression loss.
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