Search Results for author:
Found 5 papers, 4 papers with code
Spatio-Temporal Point Process for Multiple Object Tracking
As such, we propose a novel framework that can effectively predict and mask-out the noisy and confusing detection results before associating the objects into trajectories.
End-to-end video instance segmentation via spatial-temporal graph neural networks
Specifically, graph nodes representing instance features are used for detection and segmentation while graph edges representing instance relations are used for tracking.
AP-Loss for Accurate One-Stage Object Detection
This paper alleviates this issue by proposing a novel framework to replace the classification task in one-stage detectors with a ranking task, and adopting the Average-Precision loss (AP-loss) for the ranking problem.
PIoU Loss: Towards Accurate Oriented Object Detection in Complex Environments
The experimental results show that PIoU loss can dramatically improve the performance of OBB detectors, particularly on objects with high aspect ratios and complex backgrounds.
Towards Accurate One-Stage Object Detection with AP-Loss
For this purpose, we develop a novel optimization algorithm, which seamlessly combines the error-driven update scheme in perceptron learning and backpropagation algorithm in deep networks.
