Abstract
In recent years, multi-stage detectors improve the accuracy of object detection to a new level. However, due to multiple stages, these methods typically fall short in the inference speed. To alleviate this problem, we propose a novel object detector—Guided Refine-Head, which is made up of a newly proposed detection network called Refine-Head and a knowledge-distillation-like loss function. Refine-Head is a two-stage detector, and thus Refine-Head has faster inference speed than multi-stage detectors. Nonetheless, Refine-Head is able to predict bounding boxes for incremental IoU thresholds like a multi-stage detector. In addition, we use knowledge-distillation-like loss function to guide the training process of Refine-Head. Therefore, besides fast inference speed, the proposed Guided Refine-Head also has competitive accuracy. Abundant ablation studies and comparative experiments on MS-COCO 2017 validate the superiority of the proposed Guided Refine-Head. It is worth noting that Guided Refine-Head achieves the AP of 38.0% at 10.4 FPS, surpassing Faster R-CNN by 1.8% at the similar speed.
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Zeng, L., Song, Y., Wang, W. (2020). Guided Refine-Head for Object Detection. In: Ro, Y., et al. MultiMedia Modeling. MMM 2020. Lecture Notes in Computer Science(), vol 11961. Springer, Cham. https://doi.org/10.1007/978-3-030-37731-1_17
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DOI: https://doi.org/10.1007/978-3-030-37731-1_17
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