CVPR2022 (Oral) - Rethinking Semantic Segmentation: A Prototype View

Rethinking Semantic Segmentation: A Prototype View

Rethinking Semantic Segmentation: A Prototype View,
Tianfei Zhou, Wenguan Wang, Ender Konukoglu and Luc Van Gool
CVPR 2022 (Oral) (arXiv 2203.15102)

News

  • [2022-04-19] Release the code based on openseg.pytorch!
  • [2022-03-31] Paper link updated!
  • [2022-03-12] Repo created. Paper and code will come soon.

Abstract

Prevalent semantic segmentation solutions, despite their different network designs (FCN based or attention based) and mask decoding strategies (parametric softmax based or pixel-query based), can be placed in one category, by considering the softmax weights or query vectors as learnable class prototypes. In light of this prototype view, this study uncovers several limitations of such parametric segmentation regime, and proposes a nonparametric alternative based on non-learnable prototypes. Instead of prior methods learning a single weight/query vector for each class in a fully parametric manner, our model represents each class as a set of non-learnable prototypes, relying solely on the mean features of several training pixels within that class. The dense prediction is thus achieved by nonparametric nearest prototype retrieving. This allows our model to directly shape the pixel embedding space, by optimizing the arrangement between embedded pixels and anchored prototypes. It is able to handle arbitrary number of classes with a constant amount of learnable parameters.We empirically show that, with FCN based and attention based segmentation models (i.e., HR-Net, Swin, SegFormer) and backbones (i.e., ResNet, HRNet, Swin, MiT), our nonparametric framework yields compelling results over several datasets (i.e., ADE20K, Cityscapes, COCO-Stuff), and performs well in the large-vocabulary situation. We expect this work will provoke a rethink of the current de facto semantic segmentation model design.

Installation

This implementation is built on openseg.pytorch. Many thanks to the authors for the efforts.

Please follow the Getting Started for installation and dataset preparation.

Performance

Cityscapes

Method Train Set Val Set Iters Batch Size mIoU Log CKPT Script
HRNet train val 80K 8 79.0 log ckpt scripts/cityscapes/hrnet/run_h_48_d_4.sh
Ours train val 80K 8 80.1 log ckpt scripts/cityscapes/hrnet/run_h_48_d_4_proto.sh

More results will come soon

Citation

@inproceedings{zhou2022rethinking,
    author    = {Zhou, Tianfei and Wang, Wenguan and Konukoglu, Ender and Van Gool, Luc},
    title     = {Rethinking Semantic Segmentation: A Prototype View},
    booktitle = {CVPR},
    year      = {2022}
}

Relevant Projects

Please also see our works [1] for a novel training paradigm with a cross-image, pixel-to-pixel contrative loss, and [2] for a novel hierarchy-aware segmentation learning scheme for structured scene parsing.

[1] Exploring Cross-Image Pixel Contrast for Semantic Segmentation - ICCV 2021 (Oral) [arXiv][code]

[2] Deep Hierarchical Semantic Segmentation - CVPR 2022 [arXiv][code]

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Comments
  • parameter numbers of the entire model?

    parameter numbers of the entire model?

    Thanks for the great work ! I want to ask a question that confuses me. In the Paper, Table 4 shows that the parameter numbers of the entire model is not increased. However, i see that in the code the prototype is inceased by class_num as follow: self.prototypes = nn.Parameter(torch.zeros(self.num_classes, self.num_prototype, in_channels), requires_grad=True) If class_num is increased, the parameter numbers of prototypes are also increased. Did I get it wrong?

  • Question about the prototype initialization?

    Question about the prototype initialization?

    Hi, thanks for the impressive work.

    After reading the paper, I have a question that how the micro-prototypes are initialized? They seem to be properly initialized so as for a reasonable solution in eq.(10).

    Cheers

  • Questions about code

    Questions about code

    Dear Author, I'm very interested in this wonderful work of yours, but due to my weak code ability, I can't find your code of online-clustering part... Could you please tell me which part of this code I should pay more attention to?

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