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Consistency-based Semi-supervised Learning for Object detection
Jisoo Jeong · Seungeui Lee · Jeesoo Kim · Nojun Kwak

Wed Dec 11 10:45 AM -- 12:45 PM (PST) @ East Exhibition Hall B + C #87

Making a precise annotation in a large dataset is crucial to the performance of object detection. While the object detection task requires a huge number of annotated samples to guarantee its performance, placing bounding boxes for every object in each sample is time-consuming and costs a lot. To alleviate this problem, we propose a Consistency-based Semi-supervised learning method for object Detection (CSD), which is a way of using consistency constraints as a tool for enhancing detection performance by making full use of available unlabeled data. Specifically, the consistency constraint is applied not only for object classification but also for the localization. We also proposed Background Elimination (BE) to avoid the negative effect of the predominant backgrounds on the detection performance. We have evaluated the proposed CSD both in single-stage and two-stage detectors and the results show the effectiveness of our method.

Author Information

Jisoo Jeong (Seoul National University)
Seungeui Lee (Seoul National University)
Jeesoo Kim (Seoul National University)
Nojun Kwak (Seoul National University)

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