HPS-Det: Dynamic Sample Assignment with Hyper-Parameter Search for Object Detection
This addresses a bottleneck in object detection for computer vision researchers, offering an incremental improvement over existing methods.
The paper tackles the problem of manual sample assignment in object detection by proposing HPS-Det, a dynamic scheme based on hyper-parameter search, which improves performance over various baselines and shows reusability across datasets and backbones.
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.