Panoptic Image Segmentation via Unified Pipeline and Attention
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Solution Overview
Problem
Conventional panoptic segmentation systems face challenges with slow inference speeds and accuracy, as they discard valuable information during the object detection phase and lack efficient sharing of computations between semantic segmentation and object detection streams.
Innovation Solution
The proposed system employs a single-shot panoptic segmentation framework that reuses discarded object-detection outputs via parameter-free global self-attention and shares computations between object detection and semantic segmentation, achieving real-time inference speeds while maintaining high accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional panoptic segmentation systems perform separate semantic segmentation and object detection, then comprehensive scene understanding is achieved, but inference speed becomes prohibitively slow
Solution Approach 1:
The patent merges semantic segmentation and object detection into a unified panoptic segmentation framework where both tasks share computational resources and processing pathways. The system processes images through a single pipeline that simultaneously generates semantic labels and object instance masks, eliminating the need for separate sequential processing and thereby achieving real-time inference speeds while maintaining comprehensive scene understanding.
Solution Approach 2:
The panoptic segmentation system performs multiple functions simultaneously: it segments the image into semantic regions, detects object instances, generates bounding boxes, and creates instance masks all within a single computational framework. This multi-functional approach allows the system to achieve comprehensive scene understanding without requiring separate specialized modules, thus improving inference speed while maintaining accuracy.
2Productivity
If conventional systems discard redundant bounding boxes during object detection, then processing time is reduced, but valuable information is lost
Solution Approach 1:
The system discards redundant bounding boxes through non-maximum suppression to reduce processing time, then recovers valuable information by using the discarded boxes as queries in the transformer-based object proposal generation. This recovery mechanism allows the system to utilize information from previously discarded boxes to generate more accurate and comprehensive object proposals, particularly for small or occluded objects.
Solution Approach 2:
The patent introduces an intermediary transformer module that acts as a bridge between the detected bounding boxes and the final object proposals. This intermediary component processes both retained and discarded bounding boxes, using attention mechanisms to selectively combine information from multiple sources, thereby recovering valuable object information that would otherwise be lost while maintaining efficient processing speeds.
3Measurement precision
If conventional systems process dense object detection outputs separately, then accurate object localization is achieved, but computational efficiency decreases
Solution Approach 1:
The system merges the processing of dense object detection outputs with the generation of object proposals by using the detected bounding boxes directly as queries in the transformer architecture. This integration eliminates separate processing stages and reduces computational complexity while maintaining accurate object localization through the attention-based mechanism that processes bounding box features efficiently.
Solution Approach 2:
The patent performs preliminary object detection and bounding box generation before the main panoptic segmentation process. These pre-processed bounding boxes are then reused as inputs to the transformer-based proposal generation, avoiding redundant processing and reducing overall computational complexity while preserving accurate object localization information for the final segmentation output.
Data Source
AI summary
Systems and methods for panoptic image segmentation are disclosed herein. One embodiment performs semantic segmentation and object detection on an input image, wherein the object detection generates a plurality of bounding boxes associated with an object in the input image; selects a query bounding box from among the plurality of bounding boxes; maps at least one of the bounding boxes in the plurality of bounding boxes other than the query bounding box to the query bounding box based on similarity between the at least one of the bounding boxes and the query bounding box to generate a mask assignment for the object, the mask assignment defining a contour of the object; compares the mask assignment with results of the semantic segmentation to produce a refined mask assignment for the object; and outputs a panoptic segmentation of the input image that includes the refined mask assignment for the object.