Open-Vocabulary Instance Segmentation With Pseudo-Mask Learning
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Solution Overview
Problem
Existing instance segmentation models require costly and time-consuming manual annotations for both pre-defined and new object categories, limiting their scalability and performance on novel categories.
Innovation Solution
An open-vocabulary instance segmentation framework using a pre-trained vision-language model to generate pseudo-mask annotations through weak supervision, eliminating the need for manual annotations and reducing overfitting by leveraging image-caption pairs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human-annotated image data is used to train segmentation models, then segmentation accuracy is improved, but annotation cost and time consumption increase
Solution Approach 1:
The patent applies preliminary action by using image-level annotations to pre-train segmentation models before deploying them for instance segmentation tasks. This pre-training phase prepares the model with general visual understanding, reducing the need for extensive instance-level annotations later.
Solution Approach 2:
The patent uses pseudo-mask annotations generated by the model as copies of ground truth labels to train subsequent models. These synthetic annotations serve as substitutes for manual instance-level annotations, significantly reducing annotation effort while maintaining segmentation quality.
2Ease of manufacture
If pre-trained vision-language models are used to generate pseudo-mask annotations, then annotation cost is reduced, but annotation quality may deteriorate
Solution Approach 1:
The patent implements feedback mechanisms where the model's pseudo-mask predictions are evaluated and used to iteratively improve annotation quality. The system refines pseudo-mask generation through multiple passes, using confidence thresholds and iterative optimization to enhance annotation accuracy.
Solution Approach 2:
The patent adjusts various parameters including confidence thresholds, iteration counts, and model architecture hyperparameters to optimize the balance between annotation efficiency and quality. By tuning these parameters, the system adapts pseudo-mask generation to achieve high-quality annotations without manual intervention.
3Productivity
If weak supervision is used instead of strong supervision, then training speed is improved, but model performance may worsen
Solution Approach 1:
The patent uses weak supervision with image-level annotations for preliminary model training, enabling fast pre-training. This preliminary phase establishes basic visual understanding before the model is fine-tuned or deployed for specific instance segmentation tasks, achieving both speed and performance.
Solution Approach 2:
The patent applies local quality by generating high-quality pseudo-mask annotations for specific regions of interest rather than requiring uniform high-quality annotations across entire images. This localized approach maintains model performance while reducing overall annotation requirements and training complexity.
Data Source
AI summary
Embodiments described herein provide an open-vocabulary instance segmentation framework that adopts a pre-trained vision-language model to develop a pipeline in detecting novel categories of instances.