Puzzle-CAM: Improved Localization Via Matching Partial And Full Features (2021-01-27T00:00:00.000000Z)

TL;DR

Puzzle-CAM, a process that minimizes differences between the features from separate patches and the whole image to discover the most integrated region in an object, can activate the overall region of an object using image-level supervision without requiring extra parameters.

Abstract

Weakly-supervised semantic segmentation (WSSS) is introduced to narrow the gap for semantic segmentation performance from pixel-level supervision to image-level supervision. Most advanced approaches are based on class activation maps (CAMs) to generate pseudo-labels to train the segmentation network. The main limitation of WSSS is that the process of generating pseudo-labels from CAMs that use an image classifier is mainly focused on the most discriminative parts of the objects. To address this issue, we propose Puzzle-CAM, a process that minimizes differences between the features from separate patches and the whole image. Our method consists of a puzzle module and two regularization terms to discover the most integrated region in an object. Puzzle-CAM can activate the overall region of an object using image-level supervision without requiring extra parameters. In experiments, Puzzle-CAM outperformed previous state-of-the-art methods using the same labels for supervision on the PASCAL VOC 2012 dataset. Code associated with our experiments is available at https://github.com/OFRIN/PuzzleCAM.

Authors

Sanghyun Jo

2 papers

In-Jae Yu

2 papers

Puzzle-CAM: Improved Localization Via Matching Partial And Full Features

TL;DR

Abstract

Authors

References18 items

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