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Branch aware assignment for object detection
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DOI：10.1007/s00371-022-02691-z.png)
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Abstract
En 中文
In object detection, deciding whether each anchor box should be assigned as a positive or negative sample is a very important procedure, known as label assignment, which greatly influences the performance of detectors. Current detectors use the Intersection-of-Union (IoU) as the criterion for assigning labels ( 0 for negative and 1 for positive ) to each anchor box while ignoring the importance of classification scores for defining samples. In this paper, we propose a novel label assignment strategy that directly assigns all ground-truths to corresponding anchor boxes based on their classification scores and determines the category belongs to each anchor box. Simultaneously, taking into account the classification accuracy and localization precision, we design a branch alignment module that enables each branch to acquire information from others as an additional supervisory signal based on the loss of different branches. Extensive competitive experiments on MS COCO benchmark demonstrate the effectiveness of our approach, which has a significant effect on the improvement of the baseline.
Keywords:
Label assignment
Object detection
Machine learning
Computer vision
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