Pyramid Scene Parsing Network
Scene parsing is challenging for unrestricted open vocabulary and diverse scenes. In this paper, we exploit the capability of global context information by different-region-based context aggregation through our pyramid pooling module together with the proposed pyramid scene parsing network (PSPNet). Our global prior representation is effective to produce good quality results on the scene parsing task, while PSPNet provides a superior framework for pixel-level prediction tasks. The proposed approach achieves state-of-the-art performance on various datasets. It came first in ImageNet scene parsing challenge 2016, PASCAL VOC 2012 benchmark and Cityscapes benchmark. A single PSPNet yields new record of mIoU accuracy 85.4% on PASCAL VOC 2012 and accuracy 80.2% on Cityscapes.
Code (67)
Tasks
Dichotomous Image SegmentationImage ClassificationLesion SegmentationReal-Time Semantic SegmentationScene ParsingSemantic SegmentationThermal Image SegmentationVideo Semantic SegmentationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
MM-Pyramid: Multimodal Pyramid Attentional Network for Audio-Visual Event Localization and Video Parsing
Recognizing and localizing events in videos is a fundamental task for video understanding. Since events may occur in auditory and visual modalities, multimodal detailed perception is essential for complete scene comprehe…
audio-visual event localizationVideo UnderstandingSemantic Flow for Fast and Accurate Scene Parsing
In this paper, we focus on designing effective method for fast and accurate scene parsing. A common practice to improve the performance is to attain high resolution feature maps with strong semantic representation. Two s…
Optical Flow EstimationReal-Time Semantic SegmentationScene ParsingSemantic SegmentationKE-GAN: Knowledge Embedded Generative Adversarial Networks for Semi-Supervised Scene Parsing
In recent years, scene parsing has captured increasing attention in computer vision. Previous works have demonstrated promising performance in this task. However, they mainly utilize holistic features, whilst neglecting …
Scene ParsingPyramidal Convolution: Rethinking Convolutional Neural Networks for Visual Recognition
This work introduces pyramidal convolution (PyConv), which is capable of processing the input at multiple filter scales. PyConv contains a pyramid of kernels, where each level involves different types of filters with var…
Action ClassificationAction Recognitionimage-classificationImage Classification+7Multi-Scale Dual-Branch Fully Convolutional Network for Hand Parsing
Recently, fully convolutional neural networks (FCNs) have shown significant performance in image parsing, including scene parsing and object parsing. Different from generic object parsing tasks, hand parsing is more chal…
Multi-class ClassificationScene Parsing