Abstract
Camouflaged object detection (COD) primarily relies on semantic or instance segmentation methods. While these methods have made significant advancements in identifying the contours of camouflaged objects, they may be inefficient or cost-effective for tasks that only require the specific location of the object. Object detection algorithms offer an optimized solution for Realistic Camouflaged Object Detection (RCOD) in such cases. However, detecting camouflaged objects remains a formidable challenge due to the high degree of similarity between the features of the objects and their backgrounds. Unlike segmentation methods that perform pixel-wise comparisons to differentiate between foreground and background, object detectors omit this analysis, further aggravating the challenge. To solve this problem, we propose a camouflage-aware feature refinement (CAFR) strategy. Since camouflaged objects are not rare categories, CAFR fully utilizes a clear perception of the current object within the prior knowledge of large models to assist detectors in deeply understanding the distinctions between background and foreground. Specifically, in CAFR, we introduce the Adaptive Gradient Propagation (AGP) module that fine-tunes all feature extractor layers in large detection models to fully refine class-specific features from camouflaged contexts. We then design the Sparse Feature Refinement (SFR) module that optimizes the transformer-based feature extractor to focus primarily on capturing class-specific features in camouflaged scenarios. To facilitate the assessment of RCOD tasks, we manually annotate the labels required for detection on three existing segmentation COD datasets, creating a new benchmark for RCOD tasks. Code and datasets are available at: this https URL.
Abstract (translated)
伪装物体检测（COD）主要依赖于语义或实例分割方法。虽然这些方法在识别伪装物体的轮廓方面取得了显著进展，但对于仅需定位特定物体的任务来说，它们可能不够高效或经济实惠。在这种情况下，对象检测算法为现实中的伪装物体检测（RCOD）提供了优化解决方案。然而，由于物体特征与其背景的高度相似性，探测伪装物体仍然是一项艰巨挑战。与执行逐像素比较以区分前景和背景的分割方法不同，目标检测器忽略了这种分析，进一步加剧了这一挑战。为解决此问题，我们提出了一个“伪装感知特性细化”（CAFR）策略。由于伪装物体并不是罕见类别，CAFR充分利用大型模型中的现有知识来帮助探测器深入理解背景与前景之间的区别。 具体而言，在CAFR中，我们引入了一个自适应梯度传播（AGP）模块，它对大检测模型的所有特征提取层进行了微调，以完全细化来自伪装场景的特定类别的特性。然后设计了稀疏特征优化（SFR）模块，该模块针对基于变压器的特征提取器进行优化，使其主要专注于在伪装场景中捕捉特定类别的特性。 为了便于评估RCOD任务，我们在三个现有的分割COD数据集中手动标注了检测所需的标签，从而创建了一个新的基准测试。相关代码和数据集可在以下链接获得：this https URL.
URL
https://arxiv.org/abs/2501.07297
