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[Review] SPG: Self-Produced Guidance (Weakly Supervised Object Localization)

Outperforms ACoL, Hide-and-Seek & CAM

Learning Process of Self-Produced Guidance (SPG)

In this story, Self-produced Guidance for Weakly-supervised Object Localization, SPG, by University of Technology Sydney, and University of Illinois Urbana-Champaign, is shortly presented.

Weakly Supervised Object Localization (WSOL) is to have object localization while without object bounding box labels, but with only image-level label, for training. In this story:

  • Self-produced Guidance (SPG) masks are proposed, which separate the foreground i.e., the object of interest, from the background to provide the classification networks with spatial correlation information of pixels.
  • The SPG masks are progressively learned and auxiliary supervised for further improvement in producing high-quality object localization maps.

This is a paper in 2018 ECCV with over 70 citations. (Sik-Ho Tsang @ Medium)

Outline

  1. Self-Produced Guidance (SPG) Overview
  2. SPG Learning

1. Self-Produced Guidance (SPG) Overview

Self-Produced Guidance (SPG)

1.1. Stem

  • Stem: is a fully convolutional network for feature extraction. It can be VGGNet or GoogLeNet, etc. The extracted feature maps FStem are then fed into the following component SPG-A.

1.2. SPG-A

  • SPG-A is a network for image-level classification, which is consisted of four convolutional blocks (i.e.A1, A2, A3 and A4), a global average pooling (GAP) layer [19] and a softmax layer.
  • This part is similar to CAM.

1.3. SPG-B

  • SPG-B is leveraged to learn Self-produced guidance masks by using the seeds of foreground and background generated from attention maps.
  • Particularly, the output features maps FA1 and FA2 of A1 and A2 are fed into the two blocks in SPG-B, respectively.
  • Each block of SPG-B contains three convolutional layers followed by a sigmoid layer. The output of SPG-B are denoted as FB1 and FB2 for the two branches, respectively.

1.4. SPG-C

  • SPG-C contains two convolutional layers with 3×3 and 1×1 kernels, followed by a sigmoid layer.
  • The component SPG-C uses the auxiliary SPG supervision to encourage the SPG-A to learn pixel-level correlations.

2. SPG Learning

2.1. Learning

  • For any image, its attention map O is extracted from A4 by simply from a classification network. This attention map usually highlights the most discriminative regions of object. (That is the one used in CAM.)
  • The initial object and background seeds can be easily obtained according to the scores in the attention maps, i.e. the binarized SPG mask:
  • M = 0: The regions with very low scores are considered as background.
  • M = 1: The regions with very high scores are considered as foreground.
  • M = 255: The rest regions are ignored during the learning process.
  • B2 is supervised by the seed map and it can learn the patterns of foreground and background.
  • The same strategy is used to find the foreground and background seeds in the output map of B2, which are used to train the B1 branch.
  • B2 is applied to learn better self-produced maps supervised by the seed map. The ignored pixels do not contribute to the loss and their gradients do not back-propagated.
  • The output of B2 is then further applied as attention maps, and better self-produced supervision masks can be calculated using the above equation.
  • The second and third layers share parameters between B1 and B2.
  • After obtaining output maps of B1 and B2, these two maps are fused to generated the final self-produced supervision map. Particularly, the average of the two maps is used to generate the self-produced guidance Mfuse.

2.2. Network Variants

  • SPG uses Inception-v3 as backbone/stem.
  • SPG-plain: Adding two convolutional layers of kernel size 3×3, stride 1, pad 1 with 1024 filters and a convolutional layer of size 1×1, stride 1 with 1000 units (200 for CUB-200–2011). Finally, a GAP layer and a softmax layer are added on the top.
  • SPG-B and SPG-C: Plain network is updated by adding these two components.

3. Experimental Results

Localization error on ILSVRC validation set
  • SPG-plain model achieves 53.71% and 41.81% of Top-1 and Top-5 localization error, mainly attribute to the structure of the Inception-v3 network.
  • The SPG strategy further reduces the localization error to Top-1 51.40% and Top-5 40.00%, outperforms GAP (CAM), Hide-and-Seek (HaS), and ACoL.
Localization error on CUB-200–2011 test set
  • The SPG approach achieves the localization error of Top-1 53.36%.
  • For SPG*, two bounding boxes are selected from the top 1st and 2nd predicted classes, and one is selected from the 3rd class. By this way, the Top-5 localization error on ILSVRC is improved to 35.05%, and that on CUB-200–2011 is improved to 40.62%.
Localization/Classi cation error on ILSVRC validation set
  • Further improvement of the localization performance is achieved by combining our localization results with the state-of-the-art classification results, i.e., ResNet and DPN.

3.2. Visualization

Illustration of the attention maps and the predicted bounding boxes of SPG (Green: predict, Red: GT)
  • The above figure shows the attention maps as well as the predicted bounding boxes.
  • SPG can highlight nearly the entire object regions and produce precise bounding boxes.
Output maps of the proposed SPG approach. (foreground (white) and background (black), and ignore the left regions (grey))
  • The above figure visualizes the output of the multiple branches in generating the self-produced guidances.
  • It can be observed the seeds usually cover small region of the object and background pixels.
  • The produced seed masks (Mask-A) are then utilized as supervision for the B2 branch.
  • B2 can learn more confident patterns of foreground and background pixels, and precisely predict the remaining foreground/background regions where leave undefined in Mask-A.
  • B1 leverages the lower level feature maps and the supervision from B2 to learn more detailed regions.
  • Finally, the self-produced guidance is obtained by fusing the two outputs of B1 and B2.

3.3. Ablation Study

Localization error on ILSVRC validation data with ground-truth labels.
  • The Top-1 error of SPG-plain is 37.32%.
  • With the assistance of the auxiliary supervision, the localization error with ground-truth labels reduces to 35.31%.
  • Without the initial seed masks, only a higher Top-1 error rate of 35.58% is obtained.
  • By removing the shared setting, the localization error rate increases from 35.31% to 36.31%.
  • After removing SPG-C, the performance becomes worse with the Top-1 error rate of 36.06%. But the localization performance with only using SPG-B is still better than the plain version. So, the branches in SPG-B can also contribute to the improvement of localization accuracy.

Reference

[2018 ECCV] [SPG]
Self-produced Guidance for Weakly-supervised Object Localization

Weakly Supervised Object Localization (WSOL)

2014 [Backprop] 2016 [CAM] 2017 [Hide-and-Seek] 2018 [ACoL] [SPG]

My Other Previous Paper Readings

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