Panoptic Segmentation via Text-Embedding Integration
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Solution Overview
Problem
Conventional panoptic segmentation methods are limited by their reliance on single-dataset training, struggling to integrate and interpret semantic information across multiple datasets with varying and potentially conflicting annotations, which restricts their usability in real-world applications requiring diverse data sources and incurs high labor costs for large, manually annotated datasets.
Innovation Solution
A method that processes images from multiple datasets using a transformer-based model, integrating multi-scale features with language-based embeddings and dataset-specific adaptations to create a unified semantic space, enabling robust panoptic segmentation across diverse datasets by generating text-embeddings for class names and using a panoptic inference algorithm to predict segmentation masks and classes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If single-dataset training is used, then the model achieves good performance on that specific dataset, but it fails to integrate and interpret semantic information across multiple datasets with varying annotations
Solution Approach 1:
The patent segments the training process by introducing dataset-specific query embeddings that separately encode information from each dataset's label space. These segmented embeddings are then integrated through attention mechanisms, allowing the model to maintain distinct dataset characteristics while achieving unified multi-dataset segmentation performance.
Solution Approach 2:
The patent introduces text-embeddings as an intermediary that bridges visual features and dataset-specific label spaces. These text-embeddings serve as a mediator that helps integrate semantic information across multiple datasets with varying annotations, resolving conflicts and enabling reliable segmentation across diverse data sources.
2Measurement precision
If large manually annotated datasets are used for training, then the model achieves higher accuracy, but labor costs and training time increase significantly
Solution Approach 1:
The patent performs preliminary action by pre-computing text-embeddings for class names from each dataset's label space before the main training process. This preprocessing step creates ready-to-use semantic representations that accelerate training and reduce the need for extensive manual annotation during the main training phase.
Solution Approach 2:
The patent uses text-embeddings as a copy or representation of semantic information that can be integrated without requiring direct access to large amounts of manually annotated data. This copying mechanism allows the model to learn from multiple datasets efficiently by leveraging textual semantic representations instead of requiring proportional manual annotations for each dataset.
3Adaptability or versatility
If multiple datasets with unique label spaces are integrated, then the model's applicability to diverse environments improves, but inconsistencies and overlaps in label spaces create challenges
Solution Approach 1:
The patent uses text-embeddings as an intermediary layer that simplifies the integration of multiple label spaces. Instead of directly combining complex dataset-specific label spaces, the model translates them into a unified text-embedding space, reducing integration complexity while maintaining adaptability to diverse environments.
Solution Approach 2:
The patent creates a unified semantic space through text-embeddings that serves as a universal representation across all datasets. This universal space allows the model to handle multiple datasets with unique label spaces using a single integrated framework, reducing complexity while maintaining versatility.
Data Source
AI summary
Systems and methods are provided for multi-dataset panoptic segmentation, including processing received images from multiple datasets to extract multi-scale features using a backbone network, each of the multiple datasets including a unique label space, generating text-embeddings for class names from the unique label space for each of the multiple datasets, and integrating the text-embeddings with visual features extracted from the received images to create a unified semantic space. A transformer-based segmentation model is trained using the unified semantic space to predict segmentation masks and classes for the received images, and a unified panoptic segmentation map is generated from the predicted segmentation masks and classes by performing inference using a panoptic interference algorithm.