Multi-source Panoptic Feature Pyramid Network for Robust Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional panoptic segmentation systems are limited by being trained on a single-source dataset, which restricts their ability to handle a large number of background classes and instance categories, making them less robust and scalable for panoptic segmentation tasks.
Innovation Solution
A multi-source panoptic feature pyramid network (FPN) architecture is employed, allowing for the training of an object detection branch, semantic segmentation branch, and instance segmentation branch on different datasets, enabling the identification and segmentation of a large number of background 'stuff' and foreground 'thing' objects in an image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional systems are trained on a single dataset for semantic segmentation or instance segmentation, then the training process is simple and focused, but the system can only handle a relatively small number of categories and lacks robustness for panoptic segmentation
Solution Approach 1:
The patent implements a unified panoptic segmentation network that performs both semantic segmentation and instance segmentation simultaneously through shared feature extraction layers. The network uses a common backbone (e.g., FPN) that processes images to generate features used by both segmentation tasks, enabling the system to handle multiple categories across different segmentation types while maintaining architectural efficiency
Solution Approach 2:
The patent divides the panoptic segmentation task into two distinct output branches: semantic segmentation branch for background classes and instance segmentation branch for foreground objects. Each branch has its own decoder and loss function, allowing specialized processing while sharing the computational backbone, thus resolving the contradiction between handling multiple categories and maintaining training simplicity
2Reliability
If a single dataset is used for training, then the training data requirements are met with one source, but the system lacks robustness and scalability to large semantic and instance datasets
Solution Approach 1:
The patent merges multiple training datasets into a unified training framework where semantic segmentation data and instance segmentation data are combined. The network is trained simultaneously on both types of data with appropriate masking and loss functions, allowing the system to learn from diverse sources and improve robustness while efficiently utilizing the total available training data volume
3Measurement precision
If separate systems are used for detection and segmentation, then each system can be optimized independently, but the overall system complexity increases and integration becomes difficult
Solution Approach 1:
The patent implements a unified panoptic segmentation network that performs both semantic segmentation and instance segmentation simultaneously through shared feature extraction layers. The network uses a common backbone (e.g., FPN) that processes images to generate features used by both segmentation tasks, enabling the system to handle multiple categories across different segmentation types while maintaining architectural efficiency
Solution Approach 2:
The shared feature pyramid network acts as an intermediary between the input image and the two segmentation outputs. Instead of having separate detection and segmentation systems, the FPN serves as a common feature extractor that feeds both semantic and instance segmentation decoders, simplifying integration while maintaining the ability to achieve high precision in both tasks
Data Source
AI summary
Systems and methods for image processing are described. Embodiments of the present disclosure receive an image having a plurality of object instances; encode the image to obtain image features; decode the image features to obtain object features; generate object detection information based on the object features using an object detection branch, wherein the object detection branch is trained based on a first training set using a detection loss; generate semantic segmentation information based on the object features using a semantic segmentation branch, wherein the semantic segmentation branch is trained based on a second training set different from the first training set using a semantic segmentation loss; and combine the object detection information and the semantic segmentation information to obtain panoptic segmentation information that indicates which pixels of the image correspond to each of the plurality of object instances.