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Any-scale Object Detection using Arbitrary-scaled Images

arXiv 2610.04346 ↗First posted 3 Oct 2026cs.CVcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 42 of its 57 references that are in the map. First posted on arXiv 3 Oct 2026.

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42 of 57 references.

References · 42

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30 more
  1. 2018Recovering Realistic Texture in Image Super-resolution by Deep Spatial Feature Transformcited by 371
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  6. 2019Meta-SR: A Magnification-Arbitrary Network for Super-Resolutioncited by 181
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  16. 2021UltraSR: Spatial Encoding is a Missing Key for Implicit Image Function-based Arbitrary-Scale Super-Resolutioncited by 57
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  19. 2023Cascaded Local Implicit Transformer for Arbitrary-Scale Super-Resolutioncited by 52
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  22. 2021SRWarp: Generalized Image Super-Resolution under Arbitrary Transformationcited by 27
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  25. 2024A DeNoising FPN With Transformer R-CNN for Tiny Object Detectioncited by 12
  26. 2022CUF: Continuous Upsampling Filterscited by 8
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