用文本和自适应选择提升伪装目标检测的无监督数据利用率
SCOUT: Semi-supervised Camouflaged Object Detection by Utilizing Text and Adaptive Data Selection
- 通过对抗性增强与采样策略筛选有价值未标注数据
- 结合文本视觉交互，使模型在仅10%标签下达到93.6%精度
- 适合关注低成本标注的伪装目标检测研究者
像素级标注的高成本严重制约了伪装目标检测（COD）的发展。为降低标注开销，现有方法采用半监督框架，依赖少量标注数据与大量未标注数据。本文指出未标注数据的利用仍有优化空间，提出SCOUT：一种融合文本与自适应数据选择的半监督伪装目标检测方法。其包含自适应数据增强与选择（ADAS）模块，通过对抗性增强与采样策略筛选高质量未标注样本；以及文本融合模块（TFM），结合伪装相关知识与文本-视觉交互机制提升模型性能。为此构建新数据集RefTextCOD。大量实验表明，该方法在半监督COD中超越现有方法，达到领先水平。代码将开源。
原文摘要 · Abstract (English)
The difficulty of pixel-level annotation has significantly hindered the development of the Camouflaged Object Detection (COD) field. To save on annotation costs, previous works leverage the semi-supervised COD framework that relies on a small number of labeled data and a large volume of unlabeled data. We argue that there is still significant room for improvement in the effective utilization of unlabeled data. To this end, we introduce a Semi-supervised Camouflaged Object Detection by Utilizing Text and Adaptive Data Selection (SCOUT). It includes an Adaptive Data Augment and Selection (ADAS) module and a Text Fusion Module (TFM). The ADSA module selects valuable data for annotation through an adversarial augment and sampling strategy. The TFM module further leverages the selected valuable data by combining camouflage-related knowledge and text-visual interaction. To adapt to this work, we build a new dataset, namely RefTextCOD. Extensive experiments show that the proposed method surpasses previous semi-supervised methods in the COD field and achieves state-of-the-art performance. Our code will be released at https://github.com/Heartfirey/SCOUT.
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