Panoptic Segmentation Using Semantic-Instance Relational Attention
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Solution Overview
Problem
Conventional image segmentation techniques, such as semantic and instance segmentation, are often implemented separately, leading to increased computational complexity and reduced accuracy due to the lack of integration of semantic and instance contexts, particularly in tasks requiring deeper understanding of image contents.
Innovation Solution
A novel framework integrating semantic and instance contexts through a panoptic, instance, and semantic relations (PISR) module, which utilizes a panoptic encoding generator, encoding weighting module, and relational attention module to capture relationships among semantic classes and instances, enhancing the accuracy of panoptic segmentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If semantic and instance segmentation are implemented separately, then each technique can be optimized independently, but computational complexity increases and segmentation accuracy decreases
Solution Approach 1:
The patent combines semantic segmentation and instance segmentation into a unified panoptic segmentation framework. The model simultaneously processes semantic features and instance features through shared encoder layers and integrated attention mechanisms, producing both semantic segmentation maps and instance segmentation masks in a single forward pass. This merging eliminates the need for separate models while capturing relationships between semantic classes and instances, thereby reducing computational complexity and improving segmentation accuracy.
2Ease of manufacture
If separate models are used for semantic and instance segmentation, then model training can be simplified, but the integration of semantic and instance contexts is lost
Solution Approach 1:
The panoptic segmentation model employs a universal architecture that performs multiple functions simultaneously. The shared encoder processes input images to extract features that are then used for both semantic segmentation and instance segmentation tasks. The relational attention module universally handles relationships between semantic classes and instances, enabling the model to capture contextual information while maintaining training efficiency through end-to-end learning with unified loss functions.
3Loss of information
If conventional segmentation methods are used, then processing speed is maintained, but the understanding of image contents at pixel level is insufficient
Solution Approach 1:
The panoptic segmentation model maintains continuous processing efficiency while achieving comprehensive image understanding. The unified architecture processes semantic and instance information simultaneously through continuous forward propagation, avoiding the need for separate processing stages. The relational attention mechanism continuously captures dependencies between pixels, semantic classes, and instances throughout the feature extraction process, enabling deep pixel-level understanding without significant speed penalties through optimized computational operations.
Data Source
AI summary
Aspects of the present disclosure relate to a novel framework for integrating both semantic and instance contexts for panoptic segmentation. In one example aspect, a method for processing image data includes: processing semantic feature data and instance feature data with a panoptic encoding generator to generate a panoptic encoding; processing the panoptic encoding to generate a panoptic segmentation features; and generating the panoptic segmentation mask based on the panoptic segmentation features.