Semantic Segmentation via Random-Walk Label Propagation
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Solution Overview
Problem
Existing semantic segmentation methods face limitations in accuracy due to the reliance on sparsely annotated datasets, which require fixed heuristics and result in poorer training performance compared to densely annotated datasets, leading to high costs.
Innovation Solution
A method that jointly trains a boundary predictor and a semantic segmentation predictor using a random-walk label propagation approach, allowing for the propagation of sparse labels to all pixels in an image, optimizing a loss function to improve the accuracy of semantic labeling without relying on fixed heuristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If sparsely annotated datasets are used for training, then production cost is reduced, but training performance and accuracy deteriorate
Solution Approach 1:
The patent introduces a boundary prediction model as an intermediary component that works alongside the semantic segmentation model. This boundary model generates boundary probability maps that guide the label propagation process, enabling effective training with sparse annotations by providing additional structural information about object boundaries without requiring dense pixel-level annotations
Solution Approach 2:
The patent replaces the traditional fixed heuristic approach (mechanical/deterministic) with a learned random-walk label propagation mechanism (stochastic/adaptive). Instead of using predetermined rules to propagate labels from sparse annotations, the system employs a differentiable random-walk process that learns optimal propagation patterns during training, achieving better accuracy with sparse data
2Measurement precision
If densely annotated datasets are used for training, then training performance and accuracy are improved, but production cost increases
Solution Approach 1:
The patent implements a partial annotation strategy where only boundary pixels and sparse interior pixels are annotated instead of full dense annotations. The boundary prediction model and random-walk propagation mechanism compensate for the missing annotations, achieving near-dense performance with significantly reduced annotation effort and cost
3Ease of operation
If fixed heuristics are used to infer dense annotations from sparse annotations, then implementation simplicity is maintained, but adaptability and accuracy are limited
Solution Approach 1:
The patent transforms the static fixed heuristic approach into a dynamic learned system. The boundary prediction model and label propagation mechanism are trained end-to-end with the semantic segmentation model, allowing them to adapt to different datasets, domains, and annotation patterns. This dynamic system automatically learns the optimal propagation behavior rather than following predetermined rules
Data Source
AI summary
Systems and methods for training semantic segmentation. Embodiments of the present invention include predicting semantic labeling of each pixel in each of at least one training image using a semantic segmentation model. Further included is predicting semantic boundaries at boundary pixels of objects in the at least one training image using a semantic boundary model concurrently with predicting the semantic labeling. Also included is propagating sparse labels to every pixel in the at least one training image using the predicted semantic boundaries. Additionally, the embodiments include optimizing a loss function according the predicted semantic labeling and the propagated sparse labels to concurrently train the semantic segmentation model and the semantic boundary model to accurately and efficiently generate a learned semantic segmentation model from sparsely annotated training images.