Class Re-Activation Maps for Weakly-Supervised Semantic Segmentation

03/02/2022
by   Zhaozheng Chen, et al.
0

Extracting class activation maps (CAM) is arguably the most standard step of generating pseudo masks for weakly-supervised semantic segmentation (WSSS). Yet, we find that the crux of the unsatisfactory pseudo masks is the binary cross-entropy loss (BCE) widely used in CAM. Specifically, due to the sum-over-class pooling nature of BCE, each pixel in CAM may be responsive to multiple classes co-occurring in the same receptive field. As a result, given a class, its hot CAM pixels may wrongly invade the area belonging to other classes, or the non-hot ones may be actually a part of the class. To this end, we introduce an embarrassingly simple yet surprisingly effective method: Reactivating the converged CAM with BCE by using softmax cross-entropy loss (SCE), dubbed ReCAM. Given an image, we use CAM to extract the feature pixels of each single class, and use them with the class label to learn another fully-connected layer (after the backbone) with SCE. Once converged, we extract ReCAM in the same way as in CAM. Thanks to the contrastive nature of SCE, the pixel response is disentangled into different classes and hence less mask ambiguity is expected. The evaluation on both PASCAL VOC and MS COCO shows that ReCAM not only generates high-quality masks, but also supports plug-and-play in any CAM variant with little overhead.

READ FULL TEXT

page 7

page 14

research
05/02/2023

Segment Anything is A Good Pseudo-label Generator for Weakly Supervised Semantic Segmentation

Weakly supervised semantic segmentation with weak labels is a long-lived...
research
11/22/2022

Out-of-Candidate Rectification for Weakly Supervised Semantic Segmentation

Weakly supervised semantic segmentation is typically inspired by class a...
research
04/04/2018

Normalized Cut Loss for Weakly-supervised CNN Segmentation

Most recent semantic segmentation methods train deep convolutional neura...
research
03/18/2023

Extracting Class Activation Maps from Non-Discriminative Features as well

Extracting class activation maps (CAM) from a classification model often...
research
11/20/2022

Attention-based Class Activation Diffusion for Weakly-Supervised Semantic Segmentation

Extracting class activation maps (CAM) is a key step for weakly-supervis...
research
03/30/2022

Threshold Matters in WSSS: Manipulating the Activation for the Robust and Accurate Segmentation Model Against Thresholds

Weakly-supervised semantic segmentation (WSSS) has recently gained much ...
research
12/14/2021

Uncertainty Estimation via Response Scaling for Pseudo-mask Noise Mitigation in Weakly-supervised Semantic Segmentation

Weakly-Supervised Semantic Segmentation (WSSS) segments objects without ...

Please sign up or login with your details

Forgot password? Click here to reset