AxisQ: Depth-Adaptive Channel--Frequency Quantization for Camouflaged Object Detection
Camouflaged object detection (COD) depends on weak texture and boundary cues that can be destroyed by aggressive post-training quantization (PTQ). Existing PTQ methods mainly optimize quantizers or reconstruct network blocks, while using a single precision-allocation domain throughout hierarchical backbones. Instead, we introduce AxisQ, a depth-adaptive bit-allocation framework motivated by the observation that the useful allocation axis changes with feature depth. Under a fixed per-layer average activation-bit budget, AxisQ allocates precision in the channel domain for shallow features and in the local-frequency domain for deep features, using COD-aware sensitivity to better preserve sub...
| Authors | Chi Ruan, Tianqi Li, Xin He et al. |
|---|---|
| Primary category | cs.CV |
| Published | 27 October 2026 |
| Last updated at source | 27 October 2026 |
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