Toward Minimal Misalignment at Minimal Cost in One-Stage and Anchor-Free Object Detection

• URL: http://arxiv.org/abs/2112.08902v1
• Date: Thu, 16 Dec 2021 14:22:13 GMT
• Title: Toward Minimal Misalignment at Minimal Cost in One-Stage and Anchor-Free Object Detection
• Authors: Shuaizheng Hao, Hongzhe Liu, Ningwei Wang and Cheng Xu
• Abstract summary: classification and regression branches have different sensibility to the features from the same scale level and the same spatial location. We propose a point-based prediction method, which is based on the assumption that the high classification confidence point has the high regression quality, leads to the misalignment problem. We aim to resolve the phenomenon at minimal cost: a minor adjustment of the head network and a new label assignment method replacing the rigid one.
• Score: 6.486325109549893
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Common object detection models consist of classification and regression branches, due to different task drivers, these two branches have different sensibility to the features from the same scale level and the same spatial location. The point-based prediction method, which is based on the assumption that the high classification confidence point has the high regression quality, leads to the misalignment problem. Our analysis shows, the problem is further composed of scale misalignment and spatial misalignment specifically. We aim to resolve the phenomenon at minimal cost: a minor adjustment of the head network and a new label assignment method replacing the rigid one. Our experiments show that, compared to the baseline FCOS, a one-stage and anchor-free object detection model, our model consistently get around 3 AP improvement with different backbones, demonstrating both simplicity and efficiency of our method.

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