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Computer Science > Computer Vision and Pattern Recognition

Title: Learning to Segment via Cut-and-Paste

Abstract: This paper presents a weakly-supervised approach to object instance segmentation. Starting with known or predicted object bounding boxes, we learn object masks by playing a game of cut-and-paste in an adversarial learning setup. A mask generator takes a detection box and Faster R-CNN features, and constructs a segmentation mask that is used to cut-and-paste the object into a new image location. The discriminator tries to distinguish between real objects, and those cut and pasted via the generator, giving a learning signal that leads to improved object masks. We verify our method experimentally using Cityscapes, COCO, and aerial image datasets, learning to segment objects without ever having seen a mask in training. Our method exceeds the performance of existing weakly supervised methods, without requiring hand-tuned segment proposals, and reaches 90% of supervised performance.
Subjects: Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:1803.06414 [cs.CV]
  (or arXiv:1803.06414v1 [cs.CV] for this version)

Submission history

From: Tal Remez [view email]
[v1] Fri, 16 Mar 2018 21:58:51 GMT (8904kb,D)