Semantic combined network for zero‐shot scene parsing

Semantic Context for Nonparametric Scene Parsing and Scene Classification

Our work focuses on different aspects of image representations as related to a variety of scene understanding tasks. We are interested in simple patch based representations as basic primitives and the role of semantic context as provided by different datasets. In our work, we have pursued a nonparametric approach for semantic parsing [5] which uses small patches and simple gradient, color and l...

Semantic Parsing for Text to 3D Scene Generation

We propose text-to-scene generation as an application for semantic parsing. This is an application that grounds semantics in a virtual world that requires understanding of common, everyday language. In text to scene generation, the user provides a textual description and the system generates a 3D scene. For example, Figure 1 shows the generated scene for the input text “there is a room with a c...

Pyramid Scene Parsing Network — Supplymentary Material

Hierarchical Feature For Scene Parsing Using Fully Recurrent Network

In scene parsing, the wide-range contextual information is not effectively encoded. Scene parsing provides segmentation and determines an scene into different regions associated with semantic categories. The main objective of scene parsing is to reduce semantic gap between humans and computer machines on scene understanding. The scenes parsing applications are object detection, text detection o...

Multi-Path Feedback Recurrent Neural Network for Scene Parsing

In this paper, we consider the scene parsing problem. We propose a novel Multi-Path Feedback recurrent neural network (MPF-RNN) to enhance the capability of RNNs on modeling long-range context information at multiple levels and better distinguish pixels that are easy to confuse in pixel-wise classification. In contrast to CNNs without feedback and RNNs with only a single feedback path, MPFRNN p...