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

2024-07-11

arXiv.org

AI Summary

Existing copy-paste data augmentation methods neglect semantic contextual relationships between source and target images, leading to artifacts such as category mismatches and spatial incoherence in fused objects. To address this, we propose a context-aware copy-paste augmentation framework centered on a Bidirectional Latent Information Propagation (BLIP) mechanism. BLIP implicitly models cross-image latent dependencies between source and target images, enabling annotation-free content–category alignment. The framework integrates SAM for precise foreground segmentation and YOLO for robust object localization, with BLIP orchestrating bidirectional semantic guidance during fusion. Evaluated on multiple vision benchmarks, our method significantly improves the realism and diversity of synthesized images. It consistently boosts downstream performance across classification, detection, and segmentation tasks—achieving average gains of 2.1–4.7 percentage points—demonstrating strong generalization and augmentation efficacy.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Natural Language Processing: Generation
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Large pretrained models with web data

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.

Problem

• Enhance image content with context-aware Copy-Paste augmentation
• Address contextual relevance gaps in existing Copy-Paste methods
• Automate object integration using BLIP, SAM, and YOLO

Innovation

• Context-Aware Copy Paste for image enrichment
• BLIP and SAM for automated content integration
• YOLO ensures cohesive object matching