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Enrich the content of the image Using Context-Aware Copy Paste

Qiushi Guo

TL;DR

The paper tackles context-inconsistent data augmentation in Copy-Paste methods by introducing Context-Aware Copy-Paste (CACP), which uses BLIP for content extraction, a BERT-based semantic matcher to select target categories from an Object365 gallery, and YOLO-SAM with Grad-CAM guidance for automatic, coherent segmentation and pasting. It demonstrates that CACP can augment data across classification, detection, and segmentation tasks without manual labeling, yielding robust improvements across architectures and datasets and accelerating convergence. The approach offers a scalable, annotation-free augmentation framework with practical impact for diverse CV applications and potential integration with diffusion-based synthesis in future work.

Abstract

Data augmentation remains a widely utilized technique in deep learning, particularly in tasks such as image classification, semantic segmentation, and object detection. Among them, Copy-Paste is a simple yet effective method and gain great attention recently. However, existing Copy-Paste often overlook contextual relevance between source and target images, resulting in inconsistencies in generated outputs. To address this challenge, we propose a context-aware approach that integrates Bidirectional Latent Information Propagation (BLIP) for content extraction from source images. By matching extracted content information with category information, our method ensures cohesive integration of target objects using Segment Anything Model (SAM) and You Only Look Once (YOLO). This approach eliminates the need for manual annotation, offering an automated and user-friendly solution. Experimental evaluations across diverse datasets demonstrate the effectiveness of our method in enhancing data diversity and generating high-quality pseudo-images across various computer vision tasks.

Enrich the content of the image Using Context-Aware Copy Paste

TL;DR

The paper tackles context-inconsistent data augmentation in Copy-Paste methods by introducing Context-Aware Copy-Paste (CACP), which uses BLIP for content extraction, a BERT-based semantic matcher to select target categories from an Object365 gallery, and YOLO-SAM with Grad-CAM guidance for automatic, coherent segmentation and pasting. It demonstrates that CACP can augment data across classification, detection, and segmentation tasks without manual labeling, yielding robust improvements across architectures and datasets and accelerating convergence. The approach offers a scalable, annotation-free augmentation framework with practical impact for diverse CV applications and potential integration with diffusion-based synthesis in future work.

Abstract

Data augmentation remains a widely utilized technique in deep learning, particularly in tasks such as image classification, semantic segmentation, and object detection. Among them, Copy-Paste is a simple yet effective method and gain great attention recently. However, existing Copy-Paste often overlook contextual relevance between source and target images, resulting in inconsistencies in generated outputs. To address this challenge, we propose a context-aware approach that integrates Bidirectional Latent Information Propagation (BLIP) for content extraction from source images. By matching extracted content information with category information, our method ensures cohesive integration of target objects using Segment Anything Model (SAM) and You Only Look Once (YOLO). This approach eliminates the need for manual annotation, offering an automated and user-friendly solution. Experimental evaluations across diverse datasets demonstrate the effectiveness of our method in enhancing data diversity and generating high-quality pseudo-images across various computer vision tasks.
Paper Structure (24 sections, 7 equations, 6 figures, 7 tables)

This paper contains 24 sections, 7 equations, 6 figures, 7 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Motivation
  4. Scalability and Extendability
  5. Content Discrepancy
  6. Method
  7. Problem Formulation
  8. Data preparation
  9. Context Awareness Module
  10. Copy-Paste
  11. Prompt generation
  12. Scale and Position
  13. Experiments
  14. Configuration
  15. Metric
  16. ...and 9 more sections

Figures (6)

  • Figure 1: Comparison between the Copy-Paste method (first row) and CACP (second row). The former overlooks the contextual relevance between the base and target images, leading to disharmony. Our approach leverage the semantic information using CAM(Context-Aware-Module) to alleviate this issue.
  • Figure 2: Our method's pipeline involves leveraging BLIP and BERT to select the best-matched target image from a gallery. Subsequently, the corresponding mask is obtained using YOLO and SAM. A single base-target pair can generate multiple augmented images based on user preferences.
  • Figure 3: GradCam comparison between the Copy-Paste(top row) and our context-aware copy paste(bottom row). CACP contributes more in person related vision tasks compared to copy-paste.
  • Figure 4: Comparison of SAM segmentation results using different prompts: Single bounding box prompts (upper row) tend to produce incomplete masks, while combining bounding boxes with Grad-CAM points generates more accurate and robust masks.
  • Figure 5: CACP provides gains that are robust to training configurations. We train DeepLabv3 on CamVid for varying number of epochs. The CACP is helpful under with and without pretraining configurations.
  • ...and 1 more figures