• Corpus ID: 3514546

Weakly Supervised Semantic Segmentation Using Superpixel Pooling Network

@inproceedings{Kwak2017WeaklySS,
  title={Weakly Supervised Semantic Segmentation Using Superpixel Pooling Network},
  author={Suha Kwak and Seunghoon Hong and Bohyung Han},
  booktitle={AAAI},
  year={2017}
}
We propose a weakly supervised semantic segmentation algorithm based on deep neural networks, which relies on imagelevel class labels only. [] Key Method To this end, we propose Superpixel Pooling Network (SPN), which utilizes superpixel segmentation of input image as a pooling layout to reflect low-level image structure for learning and inferring semantic segmentation. The initial annotations generated by SPN are then used to learn another neural network that estimates pixelwise semantic labels.

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References

SHOWING 1-10 OF 27 REFERENCES
Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation
TLDR
The decoupled architecture enables the algorithm to learn classification and segmentation networks separately based on the training data with image-level and pixel-wise class labels, respectively, and facilitates to reduce search space for segmentation effectively by exploiting class-specific activation maps obtained from bridging layers.
Weakly-and Semi-Supervised Learning of a Deep Convolutional Network for Semantic Image Segmentation
TLDR
Expectation-Maximization (EM) methods for semantic image segmentation model training under weakly supervised and semi-supervised settings are developed and extensive experimental evaluation shows that the proposed techniques can learn models delivering competitive results on the challenging PASCAL VOC 2012 image segmentsation benchmark, while requiring significantly less annotation effort.
From image-level to pixel-level labeling with Convolutional Networks
TLDR
A Convolutional Neural Network-based model is proposed, which is constrained during training to put more weight on pixels which are important for classifying the image, and which beats the state of the art results in weakly supervised object segmentation task by a large margin.
Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs
TLDR
This work brings together methods from DCNNs and probabilistic graphical models for addressing the task of pixel-level classification by combining the responses at the final DCNN layer with a fully connected Conditional Random Field (CRF).
Efficient Piecewise Training of Deep Structured Models for Semantic Segmentation
TLDR
This work shows how to improve semantic segmentation through the use of contextual information, specifically, ' patch-patch' context between image regions, and 'patch-background' context, and formulate Conditional Random Fields with CNN-based pairwise potential functions to capture semantic correlations between neighboring patches.
Constrained Convolutional Neural Networks for Weakly Supervised Segmentation
TLDR
This work proposes Constrained CNN (CCNN), a method which uses a novel loss function to optimize for any set of linear constraints on the output space of a CNN, and demonstrates the generality of this new learning framework.
BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentation
  • Jifeng Dai, Kaiming He, Jian Sun
  • Computer Science
    2015 IEEE International Conference on Computer Vision (ICCV)
  • 2015
TLDR
This paper proposes a method that achieves competitive accuracy but only requires easily obtained bounding box annotations, and yields state-of-the-art results on PASCAL VOC 2012 and PASCal-CONTEXT.
ScribbleSup: Scribble-Supervised Convolutional Networks for Semantic Segmentation
TLDR
This paper proposes to use scribbles to annotate images, and develops an algorithm to train convolutional networks for semantic segmentation supervised by scribbles, which shows excellent results on the PASCALCONTEXT dataset thanks to extra inexpensive scribble annotations.
Fully Convolutional Multi-Class Multiple Instance Learning
TLDR
This work proposes a novel MIL formulation of multi-class semantic segmentation learning by a fully convolutional network that exploits the further supervision given by images with multiple labels.
Hierarchically Gated Deep Networks for Semantic Segmentation
  • Guo-Jun Qi
  • Computer Science, Environmental Science
    2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
  • 2016
TLDR
A novel paradigm of multiscale deep network is developed to model spatial contexts surrounding different pixels at various scales, and shows competitive results compared with the other multi-scale deep networks on the semantic segmentation task.
...
1
2
3
...