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- W2804908254 abstract "Recent object detectors find instances while categorizing candidate regions. As each region is evaluated independently, the number of candidate regions from a detector is usually larger than the number of objects. Since the final goal of detection is to assign a single detection to each object, a heuristic algorithm, such as non-maximum suppression (NMS), is used to select a single bounding box for an object. While simple heuristic algorithms are effective for stand-alone objects, they can 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 the PASCAL VOC and MS COCO datasets." @default.
- W2804908254 created "2018-06-01" @default.
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- W2804908254 date "2018-05-28" @default.
- W2804908254 modified "2023-09-24" @default.
- W2804908254 title "Learning Instance-Aware Object Detection Using Determinantal Point Processes" @default.
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- W2804908254 doi "https://doi.org/10.48550/arxiv.1805.10765" @default.
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