Fully Convolutional Networks with Cross-layer Concatenation and Multi-Scale Prediction for Semantic Segmentation
碩士 === 國立清華大學 === 資訊工程學系 === 104 === Semantic image segmentation aims to assign a semantic label to each pixel in an image. Recent state-of-the-art approaches are mainly based on Convolutional Neural Networks. Although these approaches achieve outstanding performance, they adopt very complex CNN mod...
Main Authors: | Shih, Tun-Huai, 史敦槐 |
---|---|
Other Authors: | Hsu, Chiou-Ting |
Format: | Others |
Language: | en_US |
Published: |
2016
|
Online Access: | http://ndltd.ncl.edu.tw/handle/09714437432732089706 |
Similar Items
-
Multi-Layer Convolutional Features Concatenation With Semantic Feature Selector for Vein Recognition
by: Zaiyu Pan, et al.
Published: (2019-01-01) -
Semantic Segmentation via Global Convolutional Network and Concatenated Feature Maps
by: Wang, Chuan-Kai, et al.
Published: (2018) -
Parallel Fully Convolutional Network for Semantic Segmentation
by: Jian Ji, et al.
Published: (2021-01-01) -
Fully Convolutional Pyramidal Networks for Semantic Segmentation
by: Fengxiao Li, et al.
Published: (2020-01-01) -
Multi-Scale Convolutional Features Network for Semantic Segmentation in Indoor Scenes
by: Yanran Wang, et al.
Published: (2020-01-01)