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.

The paper presents a semi-supervised approach for object detection that extends the consistency regularization used for image classification [14] for object detection. Concretely, it proposes using consistency losses for both classification and localization, as well as a background elimination technique that alleviates the class imbalance inherent to object detection. They evaluate their approach with two types of detectors (single and two-stage) on PASCAL VOT 2007 with unlabeled data from VOT2012 and COCO. Pros: The approach is novel, as far as I know no previous work addresses semi-supervised learning with consistency regularization for object detection. The use of JS divergence over L2 distance is justified and shown experimentally.

This paper introduces a semi-supervised approach for object detection that extends the consistency regularization used for image classification for object detection. The proposed approach is novel and interesting. The evaluation part can be improved to make the comparison more convincing, as suggested by several reviewers.

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.

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.