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(CVPR 2017) WILDCAT:Weakly Supervised Learning of Deep ConvNets for Image Classification, Pointwise Localization and Segmentation

Keyword [Spatial Pooling] [Classwise Pooling]

Durand T , Mordan T , Thome N , et al. WILDCAT: Weakly Supervised Learning of Deep ConvNets for Image Classification, Pointwise Localization and Segmentation[C]// 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2017.



1. Overview


In this paper, it proposes WILDCAT

  • FCN backbone
  • Multi-Map WSL transfer layer
  • Wildcat Pooling


  • Max Pooling (MP)
  • Global Average Pooling (GAP)
  • LSE Pooling
  • Compact Bilinear Pooling



2. Architecture


2.1. FCN

  • ResNet101. output [W/32, H/32, 2048]
  • remove GAP and FC
  • replace with WSL transfer amd wildcat pooling layers

2.2. Multi-map Transfer Layer



  • per class through 1x1 Conv
  • (h, w, c) – (h, w, m*c)

2.3. Wildcat Pooling

2.3.1. Classwise Pooling



  • (h, w, mc) – (h, w, c)

2.3.2. Spatial Pooling



  • for a class map (h, w, 1)
  • average k+ max point
  • average k- max point
  • α. trade off
  • hypothesize that maximum scoring regions are more useful for classification
    • With α < 1 Wildcat should focus more on discriminating regions and then better localize features than with α=1

2.4. Training

  • input a single image

2.5. Inference

  • classification. input a single image
  • localization. extract the region with maximum score for each class
  • segmentation. take the class with maximum score at eachlocation independently or apply CRF



3. Experiments


3.1. Details

  • M = 4, α = 0.7
  • image. 448x448

3.2. Experiments

3.2.1. Classification







3.2.2. WSL