Mask R-CNN Mask R-CNN
Paper summary _Objective:_ Image segmentation and pose estimation with an extension of Faster R-CNN. _Dataset:_ [COCO]( and [Cityscapes]( ## Inner workings: The core operator of Faster R-CNN is the _RoIPool_ which performs coarse spatial quantization for feature extraction but introduce misalignment for pixel-pixel comparison which is what segmentation is. The paper introduce a new layer _RoIAlign_ that faithfully preserves exact spatial location. One important point is that mask and class prediction are decoupled, the segmentation is proposed for each class without competing and the class predictor finally elects the winner. ## Architecture: Based on Faster R-CNN but with an added mask subnetwork that computes a segmentation mask for each class. Different feature extractors and proposers are tried, see two examples below: [![screen shot 2017-05-22 at 7 25 04 pm](]( ## Results: Runs at about 200ms per frame on a GPU for segmentation (2 days training on a single 8-GPU) and 5 fps for pose estimation. Very impressive segmentation and pose estimation: [![screen shot 2017-05-22 at 7 26 57 pm 1](]( [![screen shot 2017-05-22 at 7 29 26 pm](](

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