Weakly Supervised Learning with Deep Convolutional Neural Networks for Semantic Segmentation: Understanding Semantic Layout of Images with Minimum Human Supervision

2017 ◽  
Vol 34 (6) ◽  
pp. 39-49 ◽  
Author(s):  
Seunghoon Hong ◽  
Suha Kwak ◽  
Bohyung Han
IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 20449-20458
Author(s):  
Yongsheng Liu ◽  
Wenyu Chen ◽  
Hong Qu ◽  
Tianlei Wang ◽  
Jiangzhou Ji ◽  
...  

Symmetry ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 427 ◽  
Author(s):  
Sanxing Zhang ◽  
Zhenhuan Ma ◽  
Gang Zhang ◽  
Tao Lei ◽  
Rui Zhang ◽  
...  

Semantic image segmentation, as one of the most popular tasks in computer vision, has been widely used in autonomous driving, robotics and other fields. Currently, deep convolutional neural networks (DCNNs) are driving major advances in semantic segmentation due to their powerful feature representation. However, DCNNs extract high-level feature representations by strided convolution, which makes it impossible to segment foreground objects precisely, especially when locating object boundaries. This paper presents a novel semantic segmentation algorithm with DeepLab v3+ and super-pixel segmentation algorithm-quick shift. DeepLab v3+ is employed to generate a class-indexed score map for the input image. Quick shift is applied to segment the input image into superpixels. Outputs of them are then fed into a class voting module to refine the semantic segmentation results. Extensive experiments on proposed semantic image segmentation are performed over PASCAL VOC 2012 dataset, and results that the proposed method can provide a more efficient solution.


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