Chained Predictions Using Convolutional Neural Networks
This work addresses spatial localization problems in computer vision, such as human pose estimation, with an incremental adaptation of existing sequence-to-sequence models.
The paper tackles structured output prediction in vision tasks by adapting a sequence-to-sequence model where output variables are predicted sequentially, depending on both input and previous predictions, and applies it to spatial localization using CNNs and multi-scale deconvolutional architectures. It achieves top-performing results on human pose estimation from single images and videos.
In this paper, we present an adaptation of the sequence-to-sequence model for structured output prediction in vision tasks. In this model the output variables for a given input are predicted sequentially using neural networks. The prediction for each output variable depends not only on the input but also on the previously predicted output variables. The model is applied to spatial localization tasks and uses convolutional neural networks (CNNs) for processing input images and a multi-scale deconvolutional architecture for making spatial predictions at each time step. We explore the impact of weight sharing with a recurrent connection matrix between consecutive predictions, and compare it to a formulation where these weights are not tied. Untied weights are particularly suited for problems with a fixed sized structure, where different classes of output are predicted in different steps. We show that chained predictions achieve top performing results on human pose estimation from single images and videos.