Publikation
CACP: Context-Aware Copy-Paste to Enrich Image Content for Data Augmentation
Qiushi Guo; Shaoxiang Wang; Chun-Peng Chang; Jason Raphael Rambach
In: Proceedings of the 1st Workshop on Exploring the Next Generation of Data. CVPR Workshop on Exploring the Next Generation of Data (NeXD-25), located at CVPR 2025, June 11, Nashville, TN, USA, IEEE, 2025.
Zusammenfassung
Data augmentation is a widely used technique in deep
learning, encompassing both pixel-level and object-level
manipulations of images. Among these techniques,
Copy-Paste stands out as a simple yet effective method.
However, current Copy-Paste approaches either overlook
the contextual relevance between source and target images,
leading to inconsistencies in the generated outputs, or
heavily depend on manual annotations, which limits their
scalability for large-scale automated image generation.
To address these limitations, we propose a context-aware
approach that integrates Bidirectional Latent Information
Propagation (BLIP) for extracting content from source
images. By aligning the extracted content with category
information, our method ensures coherent integration of
target objects through the use of the Segment Anything
Model (SAM) and YOLO. This approach eliminates the
need for manual annotation, offering an automated and
user-friendly solution. Experimental evaluations across
various datasets and tasks demonstrate the effectiveness
of our method in enhancing data diversity and generating
high-quality pseudo-images for a wide range of computer
vision applications.