Source-linked AI summary
Control Copy-Paste: Controllable Diffusion-Based Augmentation Method for Remote Sensing Few-Shot Object Detection
Yanxing Liu, Jiancheng Pan, Bingchen Zhang
TL;DR
Few-shot remote-sensing object detection is limited by scarce annotated data and overfitting to insufficiently diverse objects and contexts. Control Copy-Paste uses conditional diffusion to inject few-shot objects into diverse contexts, with orientation alignment supporting integration across aspect ratios. On DIOR, the method improves FSOD performance by an average of 10.76%.
Problem
Limited annotated remote-sensing data makes FSOD prone to overfitting, while increasing object diversity alone overlooks the importance of contextual diversity.
Source: §I · pp. 1–2, B. What limits the performance of… · p. 2Method
Control Copy-Paste uses a class-agnostic conditional diffusion pipeline to inject few-shot novel objects into diverse contexts and aligns orientations during integration.
Source: C. Control Copy-Paste Pipeline · pp. 2, 4Results
The method improves detection performance by an average of 10.76% on the DIOR dataset.
Source: B. Result Analysis · p. 4, A. Datasets and Implementation details · p. 4Takeaways & Limitations
Contextual diversity is important for FSOD in remote-sensing images, and harmoniously integrating novel objects into varied contexts improves detection performance.
Source: §I · p. 2, B. Result Analysis · p. 4When context diversity is sufficient, object diversity becomes the primary factor limiting detector performance.
Source: §IV · p. 4
Abstract
from arXiv · showhide
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%.
I. INTRODUCTION
FSOD addresses object detection with limited training samples, but scarce and difficult-to-annotate remote sensing data causes overfitting. Control Copy-Paste increases contextual diversity by harmoniously injecting few-shot novel objects into diverse scenes with diffusion models.
- Remote sensing images are difficult to collect and annotate at scale because satellite or drone acquisition, high resolution, and complex backgrounds make labelled-data construction time-consuming.Source: §I · p. 1
- FSOD transfers knowledge from data-rich base datasets to detect novel classes from limited training samples.Source: §I · p. 1
- Diffusion-based FSOD methods generate diverse instances, but overlook contextual information that also influences detection performance.Source: §I · pp. 1–2
- Control Copy-Paste uses diffusion models to harmoniously integrate few-shot novel instances into diverse contexts, improving contextual diversity beyond traditional copy-paste.Source: §I · p. 2
A. Preliminary
FSOD uses base classes with abundant labels to learn class-agnostic features, then detects novel classes from only K annotated instances in an N-way setting.
- FSOD trains first on base classes and subsequently detects N novel classes using only K annotated instances.The dataset separates disjoint base classes Cb and novel classes Cn.Source: A. Preliminary · p. 2
B. What limits the performance of FSOD: An Analysis of Factors Influencing FSOD
Experiments decoupling instances from contexts show that both object and contextual diversity constrain FSOD performance. In remote sensing images, limited contextual diversity promotes context overfitting, while increasing either factor eventually saturates when the other remains insufficient.
- Copy-paste experiments separately vary instance and context counts to analyze their effects on FSOD performance.The enhanced data is trained using FSCE, with results presented in Fig. 2.Source: B. What limits the performance of… · p. 2
- Increasing instances fails to improve performance when contexts are limited to 20-shot per class, whereas increasing instance contexts from 20 to 40 improves FSOD performance.Source: B. What limits the performance of… · p. 2
- Detector performance improves as contexts increase, indicating that contextual diversity can prevent overfitting to specific contexts.Source: B. What limits the performance of… · p. 2
- Performance saturates when diversity in one element increases while the model remains overfit to insufficiently diverse objects or contexts.Source: B. What limits the performance of… · p. 2
C. Control Copy-Paste Pipeline
Control Copy-Paste integrates few-shot instances into diverse contexts through a class-agnostic conditional diffusion pipeline and uses the resulting synthetic data for FSOD fine-tuning. Orientation alignment and affine transformations improve instance integration across varied remote-sensing viewpoints and aspect ratios.
- The pipeline addresses context overfitting by harmoniously integrating few-shot novel instances into diverse contexts with diffusion models.Source: C. Control Copy-Paste Pipeline · p. 2
- Two-stage training learns common remote-sensing features from base classes, then fine-tunes novel-class detection with synthetic data providing diverse contexts.Source: C. Control Copy-Paste Pipeline · p. 2, Figure 3 · p. 3
- Given a reference image, context image, and integration location, the pipeline produces a synthetic training image using real or generated source scenes.Source: C. Control Copy-Paste Pipeline · p. 3
- Reference instances combine coarse DINOv2 features and fine-grained high-pass details, while affine transformations increase viewpoint diversity.Source: C. Control Copy-Paste Pipeline · pp. 3–4
- Orientation alignment matches the long edges of reference instances and target areas to reduce distortion from extreme aspect ratios.Source: C. Control Copy-Paste Pipeline · p. 4
- The approach is class-agnostic and approach-agnostic, allowing application to base data and different FSOD methods.Source: C. Control Copy-Paste Pipeline · pp. 3–4
A. Datasets and Implementation details
Experiments use DIOR’s 20 classes, split into 15 base and 5 novel classes, with K-shot novel-class training sets for K = 3, 5, 10, and 20.
- DIOR contains 20 classes divided into 15 base classes and 5 novel classes.Airplane, tennis court, train station, baseball field, and windmill are novel classes; the remaining classes are base classes.Source: A. Datasets and Implementation details · p. 4
- Novel-class training data uses K-shot samples with K = 3, 5, 10, and 20 to simulate few-shot scenes.Source: A. Datasets and Implementation details · p. 4
- Mean average precision at IoU 0.5, denoted mAP@0.5, is used for evaluation.Source: A. Datasets and Implementation details · p. 4
B. Result Analysis
On DIOR, contextual diversity improves novel-class detection for both FSOD baselines, while Control Copy-Paste can outperform traditional copy-paste by fully decoupling contexts and target instances.
- With three instances and forty contexts, the detector achieves performance comparable to training with the 20-shot dataset.Source: B. Result Analysis · p. 4
- Contextual diversity improves novel-class performance and mitigates overfitting to specific contextual patterns.Source: B. Result Analysis · p. 4
- Control Copy-Paste improves performance by up to 3.81% over copy-paste through more complete context–instance decoupling.Source: B. Result Analysis · p. 4
- Both copy-paste and Control Copy-Paste cause a slight performance drop for train station, whose extreme aspect ratios create alignment difficulties.Source: B. Result Analysis · p. 4
IV. DISCUSSION
The paper argues that object diversity alone cannot prevent contextual overfitting in remote-sensing FSOD, motivating diffusion-based augmentation that inserts few-shot targets into varied contexts. It also identifies a boundary: once context diversity is sufficient, object diversity becomes the main limiting factor.
- Increasing object diversity alone is insufficient because detectors can remain overfitted to monotonous contexts.Source: §IV · p. 4
- Control Copy-Paste uses diffusion models to harmoniously inject few-shot target objects into different contexts.Source: §IV · p. 4
- The pipeline outperforms copy-paste across various FSOD approaches.Source: §IV · p. 4
- When contextual diversity is sufficient, object diversity becomes the primary factor limiting detector performance.Source: §IV · p. 4