English(EN) Consensus-Aware Multi-Source Fusion for Reference-Guided Camouflaged Object Detection

新框架通过多源融合增强伪装目标检测

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-01 04:00

研究人员开发了一种名为“面向参考引导的伪装目标检测的共识感知多源融合”的新框架。该方法通过使用辅助参考样本来解决分割与周围环境融为一体的目标所面临的挑战。该框架名为参考条件双骨干融合（RCDF），集成了可训练的PVTv2特征与冻结的DINOv3表示，并采用参考条件相关性来选择相关的基础模型证据。它还通过共识机制聚合来自多个参考的信息，并在适当的语义级别注入参考数据。 AI

影响引入了一种伪装目标检测的新方法，有望在专业的计算机视觉应用中提高性能。

排序理由这是一篇详细介绍特定计算机视觉任务新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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报道来源 [1]

arXiv cs.CV TIER_1 English(EN) · Junyang Xia, Luocheng Zhang, Wenwen Pan, Chifeng Zhu, Yang Yang, Xinchun Liu, Jiajun Ding · 2026-10-01 04:00

面向参考引导的伪装目标检测的共识感知多源融合

arXiv:2609.38747v1 Announce Type: new Abstract: Reference-guided camouflaged object detection aims to segment a target whose visual appearance closely resembles its surroundings by exploiting auxiliary reference samples. The task remains difficult because reference samples contai…

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报道来源 [1]

面向参考引导的伪装目标检测的共识感知多源融合

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