Open-Vocabulary Panoptic Segmentation With Single-Stage Feature Sharing
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Solution Overview
Problem
Existing open-vocabulary panoptic segmentation methods are inefficient and perform poorly due to reliance on two-stage frameworks that duplicate feature extraction and mismatched input resolutions, leading to high computational costs and sub-optimal performance.
Innovation Solution
Implementing a single-stage framework using a shared, frozen convolutional neural network-based vision-language model backbone for both mask generation and classification, which maintains pre-trained image-text feature alignment and supports higher-resolution inputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If two-stage frameworks are used for open-vocabulary panoptic segmentation, then classification capability is improved, but computational cost and processing time increase due to duplicate feature extraction
Solution Approach 1:
The patent merges the mask generation module and classification module into a single integrated framework that shares a common feature extraction backbone. This eliminates the duplicate feature extraction process in two-stage frameworks while maintaining both segmentation and classification capabilities through geometric ensembling of predictions from shared features.
Solution Approach 2:
The shared vision-language model backbone serves multiple functions simultaneously: it extracts features for both mask generation and classification tasks. This multi-functional design allows the system to perform panoptic segmentation and open-vocabulary classification without requiring separate feature extraction processes.
2Measurement precision
If two-stage frameworks are used for open-vocabulary panoptic segmentation, then classification capability is improved, but device complexity increases
Solution Approach 1:
The patent combines multiple functional modules (feature extraction, mask generation, classification) into a unified single-stage framework. This reduces the overall system complexity by eliminating the need for separate processing stages and their associated interfaces and data transfers.
Solution Approach 2:
The shared vision-language model backbone performs multiple functions (feature extraction for both segmentation and classification) simultaneously, reducing the number of separate components needed and simplifying the overall system architecture.
3Manufacturing precision
If higher-resolution inputs are used, then segmentation accuracy is improved, but computational cost increases
Solution Approach 1:
The integrated framework processes high-resolution images once through a shared backbone, avoiding the multiple processing passes required by two-stage frameworks. This reduces the total computational cost of handling high-resolution inputs while maintaining segmentation accuracy.
Solution Approach 2:
The single-stage framework maintains continuous feature extraction from high-resolution inputs without interruption or re-processing, allowing the system to fully utilize the detailed information in high-resolution images for both mask generation and classification without redundant computational steps.
Data Source
AI summary
Single-stage frameworks for open-vocabulary panoptic segmentation are provided. One aspect provides a computing system comprising a processor and memory storing instructions that, when executed by the processor, cause the processor to: receive an image; extract a plurality of feature maps from the image using a convolutional neural network-based vision-language model; generate a plurality of pixel features from the plurality of feature maps; generate a plurality of mask predictions from the plurality of pixel features; generate a plurality of in-vocabulary class predictions corresponding to the plurality of mask predictions using the plurality of pixel features; generate a plurality of out-of-vocabulary class predictions using the plurality of feature maps; perform geometric ensembling on the plurality of in-vocabulary class predictions and the plurality of out-of-vocabulary class predictions to generate a plurality of final class predictions; and output the plurality of mask predictions and the plurality of final class predictions.