  We present a weakly supervised instance segmentation algorithm based on deep
community learning with multiple tasks. This task is formulated as a
combination of weakly supervised object detection and semantic segmentation,
where individual objects of the same class are identified and segmented
separately. We address this problem by designing a unified deep neural network
architecture, which has a positive feedback loop of object detection with
bounding box regression, instance mask generation, instance segmentation, and
feature extraction. Each component of the network makes active interactions
with others to improve accuracy, and the end-to-end trainability of our model
makes our results more robust and reproducible. The proposed algorithm achieves
state-of-the-art performance in the weakly supervised setting without any
additional training such as Fast R-CNN and Mask R-CNN on the standard benchmark
dataset. The implementation of our algorithm is available on the project
webpage: https://cv.snu.ac.kr/research/WSIS_CL.
