English(EN) AxisQ: Depth-Adaptive Channel--Frequency Quantization for Camouflaged Object Detection

AxisQ 框架增强伪装目标检测模型

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-28 04:00

研究人员开发了 AxisQ，一个新颖的深度自适应量化框架，旨在改进伪装目标检测 (COD) 模型。该方法智能分配精度，利用通道域处理浅层特征，利用局部频率域处理深层特征，以更好地保留细微的前景区域和模糊边界。实验表明，AxisQ 在低比特宽度下显著提高了精度，并且在 CFRN 和 CamoFormer-P 等模型上取得了显著的提升。 AI

影响这项研究可能带来更高效、更准确的计算机视觉模型，用于检测细微或隐藏物体的任务。

排序理由该集群描述了一篇关于特定计算机视觉任务新颖技术方法的新研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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报道来源 [1]

arXiv cs.CV TIER_1 English(EN) · Chi Ruan, Tianqi Li, Xin He, Xue Geng, Ming-Ming Cheng, Yun Liu · 2026-10-28 04:00

AxisQ：用于伪装目标检测的深度自适应通道-频率量化

arXiv:2610.36844v1 Announce Type: new Abstract: 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 …

本文如何被排名

报道来源 [1]

AxisQ：用于伪装目标检测的深度自适应通道-频率量化

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