Improve SegNet with feature pyramid for road scene parsing

Pyramid Scene Parsing Network — Supplymentary Material

Video Scene Parsing with Predictive Feature Learning: — Supplementary Material —

In this supplementary material, we provide more implementation details including the architectures and training settings of baseline models and PEARL. We also present the experimental results and analysis of PEARL on Camvid dataset, as well as more qualitative evaluations of PEARL. 1. Implementation Details Since the class distribution is extremely unbalanced in video scene parsing, we increase...

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...

SegNet: A Deep Convolutional Encoder-Decoder Architecture for Scene Segmentation

We present a novel and practical deep fully convolutional neural network architecture for semantic pixel-wise segmentation termed SegNet. This core trainable segmentation engine consists of an encoder network, a corresponding decoder network followed by a pixel-wise classification layer. The architecture of the encoder network is topologically identical to the 13 convolutional layers in the VGG...

Geometric Scene Parsing with Hierarchical LSTM

This paper addresses the problem of geometric scene parsing, i.e. simultaneously labeling geometric surfaces (e.g. sky, ground and vertical plane) and determining the interaction relations (e.g. layering, supporting, siding and affinity) between main regions. This problem is more challenging than the traditional semantic scene labeling, as recovering geometric structures necessarily requires th...