Panoptic Segmentation Refinement Network for Accuracy and Efficiency
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Solution Overview
Problem
Existing panoptic segmentation technologies face inaccuracies in detecting instances and defining boundaries between stuff and things, and they inefficiently consume computing resources due to iterative processing and high memory requirements.
Innovation Solution
A Panoptic Segmentation Refinement Network (PRN) is introduced, utilizing an encoder-decoder-based neural network to refine low-resolution masks, generating foreground masks, center maps, center offset maps, and bounding box offset maps to improve the accuracy and efficiency of panoptic segmentation by optimizing the panoptic loss function and reducing computational overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing panoptic segmentation technologies are used, then segmentation functionality is provided, but inaccuracies occur in detecting instances and defining boundaries
Solution Approach 1:
The patent segments the segmentation task into multiple components: a base panoptic segmentation model for initial predictions and a refinement network for correcting inaccuracies. The refinement network processes instance masks, stuff masks, and their boundaries separately to address specific error types, thereby improving overall segmentation accuracy while maintaining reliability through structured correction of detected instances and defined boundaries.
Solution Approach 2:
The refinement network operates as a feedback mechanism that takes the output of the base panoptic segmentation model and corrects its inaccuracies. By analyzing the predicted instance masks and stuff masks, the refinement network identifies and corrects boundary errors, instance detection failures, and classification inaccuracies, thereby improving measurement precision and reliability through iterative refinement.
2Measurement precision
If iterative processing is used to improve segmentation quality, then accuracy is improved, but computing resource consumption increases
Solution Approach 1:
Instead of using extensive iterative processing that repeatedly refines all predictions, the patent applies partial action by implementing a single-pass refinement network that selectively corrects only the most critical inaccuracies in instance masks and stuff masks. This approach achieves significant accuracy improvements while reducing computing resource consumption by avoiding unnecessary iterative computations.
Solution Approach 2:
The refinement network changes the processing parameters by operating at a coarser resolution for the refinement stage rather than maintaining full-resolution iterative processing. This parameter change allows the system to achieve improved segmentation accuracy while significantly reducing computing resource consumption and CPU/GPU intensity.
3Measurement precision
If high-resolution mask processing is used, then segmentation quality is improved, but memory requirements increase
Solution Approach 1:
The patent segments the memory processing by handling instance masks and stuff masks separately through the refinement network, allowing selective processing of only the necessary regions. This segmentation approach maintains segmentation quality through high-resolution processing where needed while reducing overall memory requirements by avoiding simultaneous loading of all high-resolution data.
Solution Approach 2:
The refinement network processes masks in a transformed feature space dimension rather than solely relying on spatial resolution. By incorporating semantic information and boundary predictions into the refinement process, the system achieves high segmentation quality without proportionally increasing memory requirements, as the feature-dimensional processing is more memory-efficient than pure spatial high-resolution processing.
Data Source
AI summary
Various disclosed embodiments are directed to refining or correcting individual semantic segmentation/instance segmentation masks that have already been produced by baseline models in order to generate a final coherent panoptic segmentation map. Specifically, a refinement model, such as an encoder-decoder-based neural network, generates or predicts various data objects, such as foreground masks, bounding box offset maps, center maps, center offset maps, and coordinate convolution. This, among other functionality described herein, improves the inaccuracies and computing resource consumption of existing technologies.