Unified Panoptic Segmentation Network with Attention Module
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Solution Overview
Problem
Conventional panoptic segmentation methods are computationally inefficient and fail to capture the complementarity of instance and semantic information, leading to mis-categorization of 'things' and 'stuff' in images.
Innovation Solution
A unified panoptic segmentation network is proposed, utilizing a feature pyramid network (FPN) structure with a Mask R-CNN framework and a semantic segmentation branch, incorporating an attention module to enhance feature maps and integrate instance and semantic information, thereby improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If separate networks trained for individual tasks are used with heuristic combination algorithms, then implementation simplicity is maintained, but computational efficiency deteriorates and complementarity of instance and semantic information is not captured
Solution Approach 1:
The patent merges separate instance segmentation and semantic segmentation networks into a unified panoptic segmentation network. The network architecture integrates instance segmentation head and semantic segmentation head that share common feature extraction components, allowing simultaneous processing of both tasks in a single forward pass. This eliminates the need for separate networks and heuristic combination algorithms, thereby improving computational efficiency while maintaining implementation feasibility through modular head designs.
2Ease of manufacture
If separate networks trained for individual tasks are used, then implementation simplicity is maintained, but accuracy in capturing complementarity of instance and semantic information deteriorates
Solution Approach 1:
The unified network architecture enables instance segmentation head and semantic segmentation head to share common feature extraction capabilities. This sharing allows the network to capture complementarity between instance and semantic information at the feature level, improving accuracy. The modular head design maintains implementation simplicity by keeping each head's processing logic separate while benefiting from shared representations.
Solution Approach 2:
The network is segmented into distinct functional components: shared feature extraction backbone, instance segmentation head, and semantic segmentation head. This segmentation allows each component to be optimized for its specific task while maintaining overall integration. The separate heads can process instance and semantic information independently after shared feature extraction, preserving task-specific accuracy while capturing complementarity through shared representations.
3Measurement precision
If feature pyramid network with attention module is used to integrate instance and semantic information, then segmentation accuracy is improved, but system complexity increases
Solution Approach 1:
The feature pyramid network serves multiple functions: it extracts features at multiple scales for both instance and semantic segmentation, and provides the attention mechanism for integrating instance and semantic information. This multi-functionality reduces the need for separate specialized components, thereby improving segmentation accuracy while limiting the increase in system complexity through efficient resource sharing.
Data Source
AI summary
A method, apparatus, non-transitory computer readable medium, and system for panoptic segmentation are described. Embodiments may generate a feature pyramid for an input image, wherein the feature pyramid comprises a plurality of feature maps at different resolution levels, apply an attention module to the feature pyramid to produce an enhanced feature map, combine the enhanced feature map with each of the plurality of feature maps to produce an enhanced feature pyramid, generate semantic information for the input image based on the enhanced feature pyramid, generate a plurality of object regions based on the enhanced feature pyramid, generate instance information for each of the plurality of object regions, and generate panoptic segmentation information for the input image based on the semantic information and the instance information for each of the plurality of object regions.