Learning instance-aware object detection using determinantal point processes

Recent object detectors localize instances and classify candidate regions simultaneously. The number of candidate regions is typically larger than the number of objects and each region is evaluated independently. To assign a single detection bounding box for each object, heuristic algorithms, such as non-maximum suppression (NMS), have been used widely. While simple heuristic algorithms are effective for stand-alone objects, they often fail to detect overlapped objects. In this paper, we address this issue by training a network to distinguish different objects using the relationship between candidate boxes. We propose an instance-aware detection network (IDNet), which can learn to extract features from candidate regions and measure their similarities. Based on pairwise similarities and detection qualities, the IDNet selects a subset of candidate bounding boxes using instance-aware determinantal point process inference (IDPP). Extensive experiments demonstrate that the proposed algorithm achieves significant improvements for detecting overlapped objects compared to existing state-of-the-art detection methods on CrowdHuman, Pascal VOC, and MS COCO datasets.