Instance Segmentation Models

Mask R-CNN

Introduced by He et al. in Mask R-CNN

Mask R-CNN extends Faster R-CNN to solve instance segmentation tasks. It achieves this by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition. In principle, Mask R-CNN is an intuitive extension of Faster R-CNN, but constructing the mask branch properly is critical for good results.

Most importantly, Faster R-CNN was not designed for pixel-to-pixel alignment between network inputs and outputs. This is evident in how RoIPool, the de facto core operation for attending to instances, performs coarse spatial quantization for feature extraction. To fix the misalignment, Mask R-CNN utilises a simple, quantization-free layer, called RoIAlign, that faithfully preserves exact spatial locations.

Secondly, Mask R-CNN decouples mask and class prediction: it predicts a binary mask for each class independently, without competition among classes, and relies on the network's RoI classification branch to predict the category. In contrast, an FCN usually perform per-pixel multi-class categorization, which couples segmentation and classification.

Source: Mask R-CNN


Paper Code Results Date Stars


Task Papers Share
Instance Segmentation 162 21.04%
Object Detection 129 16.75%
General Classification 15 1.95%
Image Segmentation 13 1.69%
Panoptic Segmentation 13 1.69%
Pose Estimation 13 1.69%
Decoder 9 1.17%
Clustering 9 1.17%
Classification 8 1.04%