DenseScout: Algorithm-System Co-design for Budgeted Tiny Object Selection on Edge Platforms
Deploying high-resolution tiny-object perception on edge platforms requires not only accurate localization, but also selecting a small set of informative patches under compute, transport, and latency constraints. We study budgeted tiny-object selection, where a frontend ranks patch centers from a lightweight proxy and a downstream detector processes only the selected regions. DenseScout is a 1.01M-parameter deployment-oriented dense-response selector that removes detector-style box regression and directly optimizes ranked patch-center prioritization. Its contribution lies in the task-specific selector formulation, the alignment among output representation, supervision, and decoding, and i...
| Authors | Zhouzhi Xiong, Zimo Zeng, Yi Chen et al. |
|---|---|
| Primary category | cs.CV |
| Published | 28 April 2026 |
| Last updated at source | 4 September 2026 |
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