The 2D heatmap-based approaches have dominated Human Pose Estimation (HPE) for years due to high performance.
In this work, we propose an effective weakly-supervised video semantic segmentation pipeline with click annotations, called WeClick, for saving laborious annotating effort by segmenting an instance of the semantic class with only a single click.
For object detection, the well-established classification and regression loss functions have been carefully designed by considering diverse learning challenges.
Motion blurry images challenge many computer vision algorithms, e. g, feature detection, motion estimation, or object recognition.
To a large extent, the privacy of visual classification data is mainly in the mapping between the image and its corresponding label, since this relation provides a great amount of information and can be used in other scenarios.
The deep learning-based visual tracking algorithms such as MDNet achieve high performance leveraging to the feature extraction ability of a deep neural network.
We use both instance-aware semantic segmentation and sparse scene flow to classify objects as either background, moving, or potentially moving, thereby ensuring that the system is able to model objects with the potential to transition from static to dynamic, such as parked cars.
This results in a system that provides reliable and drift-less pose estimations for high speed autonomous driving.