Deep Hierarchical Parsing for Semantic Segmentation
This work addresses semantic segmentation for computer vision applications, representing an incremental improvement over existing methods.
The paper tackles the problem of semantic segmentation by improving the Recursive Context Propagation Network (RCPN) through two modifications: adding classification loss for internal parse tree nodes to address bypass error paths, and using an MRF to model hierarchical dependencies. The result is state-of-the-art performance on Stanford Background, SIFT-Flow, and Daimler urban datasets.
This paper proposes a learning-based approach to scene parsing inspired by the deep Recursive Context Propagation Network (RCPN). RCPN is a deep feed-forward neural network that utilizes the contextual information from the entire image, through bottom-up followed by top-down context propagation via random binary parse trees. This improves the feature representation of every super-pixel in the image for better classification into semantic categories. We analyze RCPN and propose two novel contributions to further improve the model. We first analyze the learning of RCPN parameters and discover the presence of bypass error paths in the computation graph of RCPN that can hinder contextual propagation. We propose to tackle this problem by including the classification loss of the internal nodes of the random parse trees in the original RCPN loss function. Secondly, we use an MRF on the parse tree nodes to model the hierarchical dependency present in the output. Both modifications provide performance boosts over the original RCPN and the new system achieves state-of-the-art performance on Stanford Background, SIFT-Flow and Daimler urban datasets.