Variable-Granularity Tokenization for High-Resolution Object Detection
ViT detectors fix a uniform token grid before any learned stage. A native-resolution aerial detector must then choose between resolving few-pixel objects and staying inside compute and memory limits. We introduce VGTok, a training-free tokenizer that sets patch granularity per region from pixels, ahead of the encoder. VGTok scores each region by multi-scale morphological top-hat separability from its surround, then thresholds those scores at a per-image percentile, which fixes the token budget. A structure-tensor gate ($λ_{\min}$) refines only where two-dimensional object structure supports it, leaving one-dimensional clutter coarse. The resulting token set is a strict partition of the im...
| Authors | Khayrul Islam |
|---|---|
| Primary category | cs.CV |
| Published | 27 August 2026 |
| Last updated at source | 27 August 2026 |
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