AxisQ: Depth-Adaptive Channel--Frequency Quantization for Camouflaged Object Detection
Read the original on arXiv Computer Vision →AxisQ introduces a depth‑adaptive bit‑allocation framework for camouflaged object detection (COD) that allocates precision in the channel domain for shallow features and in the local‑frequency domain for deep features, guided by COD‑aware sensitivity. It also incorporates a boundary‑aware Hessian approximation to better preserve subtle foreground regions and ambiguous boundaries during post‑training quantization. Experiments on multiple COD models and benchmarks show significant accuracy gains at low bit widths, with relative weighted F‑measure improvements of 12.7% for CFRN and 33.3% for CamoFormer‑P on the CAMO dataset under W2A2 quantization.
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