HPS-Det: Dynamic Sample Assignment with Hyper-Parameter Search for Object Detection

• URL: http://arxiv.org/abs/2207.11539v1
• Date: Sat, 23 Jul 2022 15:13:57 GMT
• Title: HPS-Det: Dynamic Sample Assignment with Hyper-Parameter Search for Object Detection
• Authors: Ji Liu, Dong Li, Zekun Li, Han Liu, Wenjing Ke, Lu Tian, Yi Shan
• Abstract summary: We propose a novel dynamic sample assignment scheme based on hyper- parameter search. Experiments demonstrate that the resulting HPS-Det brings improved performance over different object detection baselines.
• Score: 25.71039912705784
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Sample assignment plays a prominent part in modern object detection approaches. However, most existing methods rely on manual design to assign positive / negative samples, which do not explicitly establish the relationships between sample assignment and object detection performance. In this work, we propose a novel dynamic sample assignment scheme based on hyper-parameter search. We first define the number of positive samples assigned to each ground truth as the hyper-parameters and employ a surrogate optimization algorithm to derive the optimal choices. Then, we design a dynamic sample assignment procedure to dynamically select the optimal number of positives at each training iteration. Experiments demonstrate that the resulting HPS-Det brings improved performance over different object detection baselines. Moreover, We analyze the hyper-parameter reusability when transferring between different datasets and between different backbones for object detection, which exhibits the superiority and versatility of our method.

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