真的存在伪装物体吗？面向现实场景的伪装物体检测
Is There Really a Camouflaged Object? Towards Realistic Camouflaged Object Detection
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中文总结 AI 辅助
针对现有伪装物体检测方法在开放世界中假阳性高的问题，提出现实场景基准OPC16K及存在感知的OPCNet，可在提升分割精度的同时降低负样本假阳性。
中文摘要 AI 辅助
伪装物体检测（COD）旨在分割视觉上隐藏在周围环境中的物体，近年来受到越来越多的关注。然而，大多数现有的COD方法是在封闭世界假设下开发的，即假设每个输入图像都包含一个伪装物体。该假设忽略了存在纯背景或非伪装物体的现实场景，导致现有模型在开放世界环境中部署时产生严重的假阳性结果。为解决这一局限，我们提出OPC16K，这是一个用于现实场景COD的大规模基准。OPC16K包含来自14个来源的16245张图像，被精心组织为伪装物体图像、纯背景图像和非伪装物体图像，能够全面评估分割质量和负样本拒绝能力。基于该基准，我们进一步提出OPCNet，这是一种存在感知的伪装网络，将COD从纯分割任务重新表述为物体定位与伪装存在推理的联合问题。具体而言，OPCNet引入分层存在推理以区分CO、BG和NOCOD场景，引入相似度感知的伪装关系建模以捕捉前景-背景伪装线索，引入存在感知的特征细化以利用存在预测来规范分割特征。在OPC16K上的大量实验表明，OPCNet在提出的现实COD评估协议下实现了卓越的性能，显著减少了负样本上的假阳性，同时保持了准确的伪装物体分割。代码和数据集将发布在此https URL。
英文摘要
Camouflaged object detection (COD) aims to segment objects that are visually concealed in their surroundings and has attracted increasing attention in recent years. However, most existing COD methods are developed under a closed-world assumption, where each input image is assumed to contain a camouflaged object. This assumption ignores realistic scenarios with pure backgrounds or non-camouflaged objects, causing existing models to produce severe false positives when deployed in open-world environments. To address this limitation, we propose OPC16K, a large-scale benchmark for realistic COD. OPC16K contains 16,245 images from 14 sources and is carefully organized into camouflaged-object images, pure background images, and non-camouflaged-object images, enabling comprehensive evaluation of both segmentation quality and negative-sample rejection. Based on this benchmark, we further propose OPCNet, a presence-aware camouflage network that reformulates COD from a pure segmentation task into a joint problem of object localization and camouflage existence reasoning. Specifically, OPCNet introduces hierarchical existence reasoning to distinguish CO, BG, and NOCOD scenarios, similarity-aware camouflage relation modeling to capture foreground-background camouflage cues, and existence-aware feature refinement to regulate segmentation features with existence predictions. Extensive experiments on OPC16K demonstrate that OPCNet achieves superior performance under the proposed realistic COD evaluation protocol, significantly reducing false positives on negative samples while maintaining accurate camouflaged-object segmentation. Code and dataset will be released at https://github.com/2231122/OPCOD.
