Universal Segment Embeddings for Open-Vocabulary Image Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing open-vocabulary image segmentation methods face challenges in accurately classifying image segments into text-defined categories due to limitations in utilizing image segments generated by foundation models, particularly in handling different granularities and training data constraints.
Innovation Solution
A universal segmentation embedding (USE) model is employed, which includes an auto-labeling pipeline to generate diverse segment-text pairs at various granularities and a lightweight model to align segment and text embeddings, leveraging pre-trained vision foundation models for efficient classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If end-to-end methods like SAN are used for open-vocabulary image segmentation, then the model can process images directly, but it cannot accept image segments generated by foundation models as input or prompts to assign class labels
Solution Approach 1:
The patent introduces a segment embedding head as an intermediary component that bridges foundation model-generated segments and the classification system. This head converts visual segments into embeddings that can be processed by the text-based classification model, enabling compatibility without requiring the entire model architecture to be redesigned. The intermediary translates between different representation formats while preserving the strengths of both foundation models and open-vocabulary classification.
2Measurement precision
If OVSeg uses a two-stage method that decouples image segmentation and classification, then it can process segments separately, but it is still limited in classifying segments at various granularities due to training data constraints
Solution Approach 1:
The patent creates a universal segment embedding representation that functions across multiple granularities and classification tasks. The segment embedding head generates embeddings that can represent segments at any level of detail (from fine-grained parts to coarse-grained objects) using a unified approach. This universal representation eliminates the need for granularity-specific processing pipelines and enables the system to handle diverse segmentation granularities with the same architecture.
Solution Approach 2:
The patent changes the parameter representation by transforming segments into a unified embedding space that captures hierarchical relationships. By modifying how segment features are parameterized (through the embedding head that processes patches and aggregates them), the system can adapt to different granularity levels without changing the fundamental model architecture or requiring granularity-specific training data.
3Productivity
If existing methods try to classify segments into text-defined categories, then they achieve open-vocabulary segmentation, but they face challenges in fully utilizing image segments generated by foundation models
Solution Approach 1:
The patent extracts the essential visual information from foundation model-generated segments by converting them into compact embeddings through the segment embedding head. This extraction process separates the visual feature representation from the classification decision-making, allowing the system to fully utilize foundation model segments without being constrained by their original format. The extracted embeddings can be efficiently processed and matched with text definitions for accurate classification.
Data Source
Figure 1A~1B
Figure 1C
Figure 2
AI summary
A computer-implemented system and method relates to open-vocabulary image segmentation. A set of data pairs is automatically generated using a digital image and a corresponding caption. The set of data pairs include image segments and corresponding text data. The set of data pairs includes (i) a first subset that includes object segments as the image segments and corresponding object data as the text data and (ii) a second subset that includes part segments as the image segments and corresponding part data as the text data. A universal segmentation embedding (USE) model includes an image encoder and a segment embedding head. The image encoder generates patch embeddings based on patches of the digital image. The segment embedding head generates segment embeddings based on the image segments and the patch embeddings. Semantic segmentation data is generated based on the segment embeddings.