  We present convolutional neural networks for the tasks of keypoint (pose)
prediction and action classification of people in unconstrained images. Our
approach involves training an R-CNN detector with loss functions depending on
the task being tackled. We evaluate our method on the challenging PASCAL VOC
dataset and compare it to previous leading approaches. Our method gives
state-of-the-art results for keypoint and action prediction. Additionally, we
introduce a new dataset for action detection, the task of simultaneously
localizing people and classifying their actions, and present results using our
approach.
