PyTorch implementation of the Mask-X-RCNN network proposed in the 'Learning to Segment Everything' paper by Facebook AI Research.

The paper is about Instance Segmentation given a huge dataset with only bounding box and a small dataset with both bbox and segmentation ground truths. It follows the semi-supervised learning paradigm. The base architecture is same as that of Mask-RCNN.

## Model Architecture

• The pipeline is as shown in the Figure. For little more explanation checkout this blog post (last section).
• Backproping both losses will induce a discrepancy in the weights of w_seg as for common classes between COCO and VG there are two losses (bbox and mask) while for rest classes its only one (bbox). There's a fix for this
• Fix: When back-propping the mask, compute the gradient of predicted mask weights (w_seg) wrt weight transfer function parameters $\theta$ but not bounding box weight $w_{det}^c$ .
• where tau is the transfer function.

## Implementation Details

• The model is based on the Mask-RCNN implementation from here. Thanks to him and original Keras version on which its based on! Integrate it with the pipeline from the repo to train the network!
• transfer_function in fpn_classifier_graph
• cls , box , cls+box choices for the detection weights in fpn_classifier_graph
• class-agnostic (baseline) and transfer (above diagram) modes for the Mask branch as explained in the paper.
• Optional MLP fusion (class agnostic MLP) as explained in Section 3.4 of the paper.
• stop_grad for backpropping mask loss (keeping w_det out of gradient calculation)

## Results

• I'm planning to run it on VOC+COCO soon. Will update once it's done.
• Note - The official Detectron (Caffe2) models and code are up here

## References

Hu, Ronghang, Piotr Dollár, Kaiming He, Trevor Darrell and Ross B. Girshick. “Learning to Segment Every Thing.” *CoRR*abs/1711.10370 (2017): n. pag.