[
    {
        "id": "osp-18122",
        "type": "article-journal",
        "title": "Any-scale Object Detection using Arbitrary-scaled Images",
        "author": [
            {
                "family": "Akita",
                "given": "Kazutoshi"
            },
            {
                "family": "Ukita",
                "given": "Norimichi"
            }
        ],
        "URL": "https://omanscience.com/en/articles/any-scale-object-detection-using-arbitrary-scaled-images",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "abstract": "This paper proposes any-scale object detection using arbitrary-scale super-resolution for continuously rescaling object images, while general multi-scale object detection uses discretely rescaled appearance representations. However, a naive usage of super-resolution produces many false-positive detections if many super-resolution images are independently fed into an object detector. Our method suppresses these false positives by predicting scale proposal maps, each of which represents a set of pixels appropriate for each super-resolution scale."
    }
]