用可控扩散模型增强遥感少样本检测的上下文多样性。
Control Copy-Paste: Controllable Diffusion-Based Augmentation Method for Remote Sensing Few-Shot Object Detection
- 通过条件扩散模型将稀有目标无缝注入多样背景图像。
- 在DIOR数据集上平均提升检测性能10.76%。
- 适合遥感图像少样本检测与数据增强研究者。
光学遥感图像中的少样本目标检测（FSOD）旨在仅用少量标注边界框识别稀有目标。有限的训练数据难以覆盖真实遥感场景的数据分布，导致严重的过拟合问题。现有研究开始利用扩散模型增加少样本新类别的多样性以缓解过拟合，但单纯提升对象多样性不足，因上下文同样关键；当对象多样性足够时，检测器易过拟合于单调的上下文。为此，本文提出Control Copy-Paste，一种可控制的扩散增强方法，通过引入多样化的上下文信息提升FSOD性能。具体地，我们使用条件扩散模型将少数样本的新目标无缝嵌入具有丰富上下文的图像中，并设计方向对齐策略，缓解因实例纵横比差异带来的融合失真。在公开的DIOR数据集上的实验表明，该方法平均提升检测性能10.76%。
原文摘要 · Abstract (English)
Few-shot object detection (FSOD) for optical remote sensing images aims to detect rare objects with only a few annotated bounding boxes. The limited training data makes it difficult to represent the data distribution of realistic remote sensing scenes, which results in the notorious overfitting problem. Current researchers have begun to enhance the diversity of few-shot novel instances by leveraging diffusion models to solve the overfitting problem. However, naively increasing the diversity of objects is insufficient, as surrounding contexts also play a crucial role in object detection, and in cases where the object diversity is sufficient, the detector tends to overfit to monotonous contexts. Accordingly, we propose Control Copy-Paste, a controllable diffusion-based method to enhance the performance of FSOD by leveraging diverse contextual information. Specifically, we seamlessly inject a few-shot novel objects into images with diverse contexts by a conditional diffusion model. We also develop an orientation alignment strategy to mitigate the integration distortion caused by varying aspect ratios of instances. Experiments on the public DIOR dataset demonstrate that our method can improve detection performance by an average of 10.76%.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。