Generative Model Inpainting for Annotated Data Augmentation
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Solution Overview
Problem
The process of providing pixel-wise annotations for instance segmentation datasets is labor-intensive and costly, affecting the accuracy and robustness of computer vision models, while existing diffusion models lack effective methods for inpainting in latent spaces, leading to inaccurate mask-object correspondence.
Innovation Solution
A system and method that uses a trained generative model, such as a diffusion-based inpainting model, to partially erase objects in annotated masks and fill out erased areas with a desired object class, guided by the object outline, generating additional annotated data samples without additional training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual pixel-wise annotation is used for instance segmentation datasets, then annotation accuracy is improved, but labor intensity and cost increase significantly
Solution Approach 1:
The patent uses a generative model to create synthetic annotated data samples by copying and transforming existing annotated images. The model generates new images with objects that have the same semantic labels and mask annotations as the original images, thereby replicating the annotation quality without requiring manual re-annotation. This resolves the contradiction by providing accurate annotations through synthetic generation rather than manual labor.
Solution Approach 2:
The system performs self-service by automatically generating annotated data samples using a trained generative model. The model takes existing annotated images as input and produces new annotated images with consistent labels and masks, enabling the system to annotate data autonomously without human intervention. This eliminates the labor-intensive manual annotation process while maintaining accuracy.
2Speed
If diffusion models are used for inpainting in latent spaces, then generation speed is improved, but mask-object correspondence accuracy deteriorates
Solution Approach 1:
The patent implements feedback mechanisms where the generative model continuously refines the inpainting process by comparing generated masks with original annotations. The model uses the eroded mask as guidance and adjusts the generation process to ensure that the filled-in objects maintain accurate correspondence with the original mask boundaries. This feedback loop resolves the contradiction by maintaining both speed and accuracy.
Solution Approach 2:
The system performs preliminary actions by first eroding the original mask to create an eroded mask that serves as guidance for the inpainting process. This pre-processing step establishes clear boundaries and semantic information before the actual generation occurs, ensuring that the generated objects will accurately correspond to the original mask annotations while maintaining fast generation speeds.
3Reliability
If additional annotated data samples are generated, then training robustness is improved, but data generation complexity increases
Solution Approach 1:
The generative model serves multiple functions: it generates synthetic images, creates annotated masks, and produces eroded masks for guidance. This multi-functionality reduces the need for separate complex processing steps and tools, thereby generating diverse training data without proportionally increasing system complexity. The single model handles data generation, annotation creation, and mask processing simultaneously.
Data Source
AI summary
An example system includes a processor to receive an annotated data sample comprising an object contained in an annotated mask. The processor can partially erase the object contained in the annotated mask. The processor can fill out an erased area of the object a predetermined number of times via a generative model to generate additional annotated data samples.