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P2C-COD, T-COD|伪装物体检测数据集|弱监督学习数据集
收藏arXiv2025-01-10 更新2025-01-14 收录
下载链接：
http://arxiv.org/abs/2501.06038v1
下载链接
链接失效反馈资源简介：
P2C-COD和T-COD是由复旦大学计算机科学与技术学院开发的两个新型数据集，旨在支持弱监督伪装物体检测（WSCOD）任务。P2C-COD是一个点监督数据集，通过点标注提供精确的物体位置信息，而T-COD则是一个文本监督数据集，利用文本提示标签生成伪掩码。这两个数据集结合了点标注和文本提示的优势，能够生成高质量的伪标签，用于训练弱监督模型。数据集的应用领域包括野生动物保护、医学图像分割、战场敌情检测等，旨在解决伪装物体检测中的复杂视觉模式识别问题。
提供机构：
复旦大学计算机科学与技术学院，上海智能信息处理重点实验室；复旦大学义乌研究院
创建时间：
2025-01-10
AI搜集汇总
数据集介绍
构建方式
P2C-COD和T-COD数据集的构建基于弱监督学习框架，旨在通过点标注和文本提示标签检测伪装物体。P2C-COD数据集通过随机选择每个伪装物体的一个像素进行标注，简化了标注过程，使得即使是未经验证的标注者也能在1至2秒内完成一张图像的标注。T-COD数据集则专注于识别图像中伪装物体的类型，标注过程耗时约一小时。这两个数据集均基于广泛认可的CAMO和COD10K伪装物体检测训练数据集进行重新标注，确保了数据的多样性和代表性。
特点
P2C-COD和T-COD数据集的特点在于其弱监督标注方式，显著减少了标注成本和时间。P2C-COD通过单点标注提供了精确的物体位置信息，而T-COD则通过文本提示标签提供了语义信息。这种双路径监督方式不仅提高了标注效率，还为模型训练提供了丰富的上下文信息。此外，数据集的构建充分利用了SAM、Grounding DINO和CLIP等基础模型，确保了生成的高质量伪标签能够有效支持模型的训练和优化。
使用方法
P2C-COD和T-COD数据集的使用方法分为三个阶段：分割、选择和训练。在分割阶段，利用点标注和文本提示生成候选掩码；在选择阶段，通过CLIP模型选择最可靠的掩码作为伪标签；在训练阶段，使用自监督的Vision Transformer对选定的伪标签进行训练。这种分阶段的框架不仅提高了伪标签的质量，还显著提升了模型在伪装物体检测任务中的性能。通过结合点标注和文本提示的优势，该框架在多个基准数据集上均表现出色，缩小了弱监督与全监督方法之间的性能差距。
背景与挑战
背景概述
P2C-COD和T-COD数据集是由复旦大学计算机科学与技术学院的研究团队于2025年提出的，旨在解决弱监督伪装目标检测（WSCOD）问题。该数据集通过点标注和文本提示标签的结合，提供了一种新颖的弱监督学习框架，能够在减少标注成本的同时，提升模型对伪装目标的检测能力。P2C-COD数据集采用点标注方式，每个伪装目标仅标注一个像素点，而T-COD数据集则通过文本提示标签来描述图像中的伪装目标。这一研究在计算机视觉领域具有重要意义，尤其是在野生动物保护、医学图像分割、军事侦察等实际应用中，能够有效提升伪装目标的检测精度。
当前挑战
P2C-COD和T-COD数据集在构建和应用过程中面临多重挑战。首先，伪装目标与背景的高度相似性使得检测任务极为复杂，传统的图像分割方法难以准确区分目标与背景。其次，弱监督学习依赖于稀疏标注，如何从有限的点标注或文本提示中生成高质量的伪标签是一个关键问题。在数据集构建过程中，点标注的稀疏性可能导致模型难以捕捉目标的完整结构，而文本提示的模糊性则可能引入噪声，影响模型的训练效果。此外，如何有效结合点标注和文本提示，生成可靠的伪标签，并在训练过程中动态修正错误，也是该数据集面临的主要技术挑战。
常用场景
经典使用场景
P2C-COD和T-COD数据集主要用于弱监督伪装目标检测（WSCOD）任务。这些数据集通过点标注和文本提示标签的结合，提供了一种新颖的框架，能够在缺乏密集像素级标注的情况下，生成高质量的伪标签。经典使用场景包括在复杂背景中检测伪装目标，例如野生动物保护、医学图像分割、战场敌情侦察等领域。通过点标注和文本提示的协同作用，模型能够在背景与目标高度相似的情况下，精确地分割出伪装目标。
解决学术问题
P2C-COD和T-COD数据集解决了弱监督学习中的关键问题，即如何在缺乏密集标注的情况下，生成高质量的伪标签用于模型训练。传统的伪装目标检测方法依赖于大量像素级标注，标注过程耗时且成本高昂。通过引入点标注和文本提示，该数据集显著减少了标注成本，同时提升了模型的检测精度。此外，该数据集还解决了多模态信息融合的难题，通过点标注和文本提示的互补性，提升了模型在复杂场景下的鲁棒性。
衍生相关工作
P2C-COD和T-COD数据集的提出，推动了弱监督伪装目标检测领域的研究进展。基于该数据集，研究者们开发了多种创新方法，如点引导候选生成（PCG）和合格候选判别器（QCD），这些方法显著提升了模型的性能。此外，该数据集还促进了多模态信息融合技术的发展，推动了SAM、Grounding DINO和CLIP等基础模型在伪装目标检测中的应用。相关研究不仅在学术界引起了广泛关注，还为工业界的实际应用提供了新的解决方案。
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