Superpixel Sampling Networks

  title={Superpixel Sampling Networks},
  author={V. Jampani and Deqing Sun and Ming-Yu Liu and Ming-Hsuan Yang and Jan Kautz},
Superpixels provide an efficient low/mid-level representation of image data, which greatly reduces the number of image primitives for subsequent vision tasks. Existing superpixel algorithms are not differentiable, making them difficult to integrate into otherwise end-to-end trainable deep neural networks. We develop a new differentiable model for superpixel sampling that leverages deep networks for learning superpixel segmentation. The resulting Superpixel Sampling Network (SSN) is end-to-end… 
SIN: Superpixel Interpolation Network
A deep learning-based superpixel segmentation algorithm SIN which can be integrated with downstream tasks in an end-to-end way and benefit the superpixel-based community is proposed.
Superpixel Segmentation With Fully Convolutional Networks
A novel method that employs a simple fully convolutional network to predict superpixels on a regular image grid and develops a downsampling/upsampling scheme for deep networks with the goal of generating high-resolution outputs for dense prediction tasks.
Deep Superpixel Convolutional Network for Image Recognition
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End-to-end trainable network for superpixel and image segmentation
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This paper proposes a way to implicitly integrate a superpixel scheme into CNNs, which makes it easy to use superpixels with CNNs in an end-to-end fashion, and preserves detailed information such as object boundaries in the form ofsuperpixels even when the model contains downsampling layers.
HERS Superpixels: Deep Affinity Learning for Hierarchical Entropy Rate Segmentation
This work proposes a two-stage graph-based framework for superpixel segmentation called Hierarchical Entropy Rate Segmentation (HERS), which builds a hierarchical tree structure that can produce any number of highly adaptive superpixels instantaneously.
Superpixel Segmentation Via Convolutional Neural Networks with Regularized Information Maximization
  • Teppei Suzuki
  • Computer Science, Mathematics
    ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • 2020
This work proposes an unsupervised superpixel segmentation method by optimizing a randomly-initialized convolutional neural network (CNN) in inference time and verifies the advantages quantitatively and qualitatively on BSDS500 dataset.
Superpixel segmentation with attention convolution neural network
In this method, a more complex convolutional neural network with an attention module is applied to superpixel segmentation, and the addition of the attention module allows for more accurate chunking of the images, thus yielding more comprehensive and detailed segmentation results.
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Learning Based SLIC Superpixel Generation and Image Segmentation
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SLIC Superpixels Compared to State-of-the-Art Superpixel Methods
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SEEDS: Superpixels Extracted Via Energy-Driven Sampling
A robust and fast to evaluate energy function is defined, based on enforcing color similarity between the boundaries and the superpixel color histogram, which is able to achieve a performance comparable to the state-of-the-art, but in real-time on a single Intel i7 CPU at 2.8 GHz.
Superpixels: An evaluation of the state-of-the-art
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A geometric-flow-based algorithm for computing a dense oversegmentation of an image, often referred to as superpixels, which yields less undersegmentation than algorithms that lack a compactness constraint while offering a significant speedup over N-cuts, which does enforce compactness.
This work proposes waterpixels as a general strategy for generating superpixels which relies on the marker controlled watershed transformation and introduces a spatially regularized gradient to achieve a tunable tradeoff between the superpixel regularity and the adherence to object boundaries.
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