NOISE-AVOIDANCE SAMPLING FOR ANNOTATION MISSING OBJECT DETECTION

Excellent results can be achieved using object detection with fully supervised training on large well-annotated datasets. However, the problem of missing annotations in real-world datasets can considerably reduce the performance of object detectors. In this study, we thoroughly analyze the effect of missing annotations on both positive and negative samples in object detector training. To mitigate the negative impact caused by annotation missing problem, we propose a simple yet effective method, noise-avoidance sampling, to distinguish noisy training samples and subsequently reduce their negative impact. Experiments are conducted on the PASCAL VOC 07+12 dataset with varying levels of missing annotations. The results reveal that the proposed method achieves comparable or superior performance with state-of-the-art methods.