We propose UniPose, a unified framework for human pose estimation, based on our “Waterfall” Atrous Spatial Pooling architecture, that achieves state-of-art-results on several pose estimation metrics. Current pose estimation methods utilizing standard CNN architectures heavily rely on statistical postprocessing or predefined anchor poses for joint localization. UniPose incorporates contextual seg- mentation and joint localization to estimate the human pose in a single stage, with high accuracy, without relying on statistical postprocessing methods. The Waterfall module in UniPose leverages the efficiency of progressive filter- ing in the cascade architecture, while maintaining multi- scale fields-of-view comparable to spatial pyramid config- urations. Additionally, our method is extended to UniPose- LSTM for multi-frame processing and achieves state-of-the- art results for temporal pose estimation in Video. Our re- sults on multiple datasets demonstrate that UniPose, with a ResNet backbone and Waterfall module, is a robust and efficient architecture for pose estimation obtaining state-of- the-art results in single person pose detection for both sin- gle images and videos.
Hello, sir.
I want to know the setting of learning rate. The code https://github.com/bmartacho/UniPose/blob/master/unipose.py#L52 , we can see self.step_size = 13275 , Why is the step size set to 13275 here? Or choose a larger number arbitrarily, are there any special requirements?
Hello, sir.
I want to know the setting of learning rate. The code https://github.com/bmartacho/UniPose/blob/master/unipose.py#L52 , we can see
self.step_size = 13275, Why is the step size set to 13275 here? Or choose a larger number arbitrarily, are there any special requirements?