We present Deeply Supervised Object Detector (DSOD), a framework that can learn object detectors from scratch. State-of-the-art object objectors rely heavily on the off-the-shelf networks pre-trained on large-scale classification datasets like ImageNet, which incurs learning bias due to the difference on both the loss functions and the category distributions between classification and detection tasks. Model fine-tuning for the detection task could alleviate this bias to some extent but not fundamentally..
Authors
Yurong Chen
Senior Research Director & Principle Research Scientist, Cognitive Computing Lab, Intel Labs China
Zhiqiang Shen
Zhuang Liu
Yu-Gang Jiang
Xiangyang Xue
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