Shape-Prior Instance Segmentation for Novel Object Classes
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Solution Overview
Problem
Existing instance segmentation systems struggle with accurately segmenting objects with plausible shapes and generalizing to novel classes without relying on class labels, often resulting in inefficient training and inaccurate segmentations.
Innovation Solution
An instance segmentation system that estimates object shapes using predefined example segmentations, generates shape descriptors, and refines segmentations using instance embeddings, allowing for class-agnostic object detection and efficient training on specialized hardware like TPUs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional instance segmentation systems are used without shape estimation, then the system complexity is lower, but the segmentation accuracy deteriorates due to implausible shapes and broken pieces
Solution Approach 1:
The system performs preliminary shape estimation using predefined example segmentations before final segmentation. Shape descriptors are generated in advance to guide the segmentation process, ensuring plausible shapes are maintained throughout object detection and segmentation operations.
2Manufacturing precision
If class-specific training is used for instance segmentation, then the segmentation accuracy for known classes is improved, but the adaptability to novel classes deteriorates
Solution Approach 1:
The system uses class-agnostic shape descriptors that can be applied universally across different object classes. The shape estimation mechanism works independently of specific object categories, allowing the same preprocessing steps to improve segmentation for both known and novel classes without requiring class-specific adaptations.
3Productivity
If standard hardware training is used for instance segmentation, then the device complexity is lower, but the training speed deteriorates compared to specialized hardware
Solution Approach 1:
The system segments the training process into distinct components: shape descriptor generation, instance embedding computation, and final segmentation. This modular architecture with fixed intermediate tensor dimensionalities allows each component to be independently optimized and efficiently executed on specialized hardware like TPUs, achieving 2-3x training speedup.
4Measurement precision
If detailed object class labels are used during training, then the segmentation precision for specific classes is improved, but the loss of information increases when applying to novel classes
Solution Approach 1:
The system extracts and utilizes shape information as a universal feature that transcends class labels. By focusing on geometric properties and spatial relationships rather than class-specific characteristics, the system preserves transferable information that remains valid when applied to novel object classes without requiring class label knowledge.
Data Source
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AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for performing instance segmentation by detecting and segmenting individual objects in an image. In one aspect, a method comprises: processing an image to generate data identifying a region of the image that depicts a particular object; obtaining data defining a plurality of example object segmentations; generating a respective weight value for each of the example object segmentations; for each of a plurality of pixels in the region of the image, determining a score characterizing a likelihood that the pixel is included in the particular object depicted in the region of the image using: (i) the example object segmentations, and (ii) the weight values for the example object segmentations; and generating a segmentation of the particular object depicted in the region of the image using the scores for the pixels in the region of the image.