共识感知的多源融合用于参考引导的伪装目标检测
Consensus-Aware Multi-Source Fusion for Reference-Guided Camouflaged Object Detection
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中文总结 AI 辅助
针对参考引导的伪装目标检测中参考线索不一致和表示不对齐问题，提出共识感知多源融合框架，结合RCDF、跨参考共识聚合和层次化解码，实验验证其有效性。
中文摘要 AI 辅助
参考引导的伪装目标检测旨在通过利用辅助参考样本，分割出视觉外观与其周围环境高度相似的目标。该任务仍然困难，因为参考样本包含不一致的目标线索，而通用视觉表示本质上与参考所指定的目标不对齐。为解决这些问题，我们提出了一种共识感知的多源融合框架。参考条件双骨干融合（RCDF）将可训练的PVTv2查询特征与冻结的DINOv3表示相结合，并在多尺度融合之前使用参考条件相关性来选择基础模型证据。该框架还通过跨参考共识聚合来聚合多个参考，并在与查询特征匹配的语义深度注入参考信息。大量实验证明了所提出方法的有效性。结果进一步表明，参考共识、目标条件基础特征和层次化解码在评估协议下提供了互补的改进。源代码将在论文被接收后公开。
英文摘要
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 contain inconsistent target cues, while generic visual representations are not inherently aligned with the target specified by the references. To handle these problems, we present a consensus-aware multi-source fusion framework. Reference-Conditioned Dual-Backbone Fusion (RCDF) couples trainable PVTv2 query features with frozen DINOv3 representations and uses reference-conditioned correlation to select foundation-model evidence before multi-scale fusion. The framework also aggregates multiple references through cross-reference consensus aggregation and injects reference information at semantic depths matched to the query features. Extensive experiments demonstrate the effectiveness of the proposed method. The results further show that reference consensus, target-conditioned foundation features, and hierarchical decoding provide complementary improvements under the evaluation protocol. The source code will be made publicly available upon acceptance.
发表机构
- School of Computer Science, Hangzhou Dianzi University（杭州电子科技大学计算机学院）
机构由 AI 辅助整理，请以论文原文为准。
