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Zigzag Learning for Weakly Supervised Object Detection
[article]
2018
arXiv
pre-print
This paper addresses weakly supervised object detection with only image-level supervision at training stage. Previous approaches train detection models with entire images all at once, making the models prone to being trapped in sub-optimums due to the introduced false positive examples. Unlike them, we propose a zigzag learning strategy to simultaneously discover reliable object instances and prevent the model from overfitting initial seeds. Towards this goal, we first develop a criterion named
arXiv:1804.09466v1
fatcat:22l3yozl6rfv5mazkvute2ohjq