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DeepLab: Semantic Image Segmentation

This post is a summary of Segmentation paper by Chen et al. 2016. They combine CRFs to generate a more accurate segmentation results.

Paper : Chen et al.

Challenges

There are 3 challenges in segmentation using Deep CNNs

  • reduced feature resolutions due to presence of multiple max-pooling and striding(downsampling). Feature maps resulting from this have reduced feature resolutions. [Solution]: instead of downsampling, use Upsampling to increase resolution in last few layers. (Atrous Convolution + bilinear interpolation) to recover original image resolution.
  • multiple scales objects present in image. [Solution]: Using Atrous spatial pyramid pooling, multiple filters are used to collect complementary field of view as a result capture multiple scales, including context and object.
  • presence of DCNN invariance reduces localization accuracy. [Solution]: use of fully connected CRF to assimilate fine details.

Advantages

Finally, some advantages of DeepLab

  • high speed of 8 FPS.
  • state-of-art accuracy.
  • simple structure.

DCNN based segmentation have following major categories

  • cascade of bottom-up segmentation with DCNN based region segmentation[ 7, 49, 50]
  • Coupling a convolutionally computed DCNN for image labelling and an independent segmentation. [39, 21, 51]
  • segmentation free approach by computing dense category-level pixel labels.[14, 52]

Works using CRF with DCNN

Advances in Segmentation

End-to-End training for structured prediction

  • [40, 59, 62, 64, 65] proposed methods for joint learning of DCNN and CRFs.

Weaker Supervision based methods

  • [58, 69, 70, 71] relaxes assumption of availability of pixel-level semantic annotations for training whole set.

Model Description

Model Description

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