Transformer Segmentation Training With Pseudo-Labels and Self-Attention
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Solution Overview
Problem
Convolutional Neural Networks (ConvNets) struggle to capture long-range dependencies and global context in image segmentation tasks, requiring large amounts of labeled data, which is challenging and expensive to obtain, leading to suboptimal performance and safety concerns in advanced driver assistance systems (ADAS).
Innovation Solution
A method for training a transformer model using a teacher-student paradigm with weak and strong augmentations, generating pseudo-labels, and incorporating self-attention mechanisms to align outputs, leveraging unlabeled data for improved segmentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If ConvNets are used for image segmentation, then local features can be captured effectively, but long-range dependencies and global context cannot be captured
Solution Approach 1:
The patent replaces the convolutional operation mechanism with a self-attention mechanism. Instead of using fixed-receptive-field convolutions that process local regions sequentially, the self-attention mechanism computes pairwise relationships between all image patches simultaneously, enabling global context capture without losing local feature precision. This substitution fundamentally changes how the model processes spatial relationships in images.
2Measurement precision
If ConvNets are used for high-performance segmentation, then large amounts of labeled data are required, but obtaining such data is challenging and expensive
Solution Approach 1:
The model performs self-training by generating its own training labels through the teacher-student framework. The teacher model generates pseudo-labels for unlabeled data, which then train the student model. This self-service mechanism allows the system to leverage abundant unlabeled data and progressively improve performance without requiring extensive manual annotation, thereby reducing the quantity of labeled data needed while maintaining high segmentation accuracy.
Solution Approach 2:
The teacher model is pre-trained on labeled data to establish a baseline performance level. This preliminary training creates a knowledgeable teacher that can then generate reliable pseudo-labels for unlabeled data, enabling the student model to learn from these pre-generated labels. This preliminary action with the teacher model reduces the subsequent need for additional labeled data in the main training process.
3Ease of operation
If ConvNets are deployed in ADAS, then object recognition can be performed, but performance diminishes in complex or cluttered environments
Solution Approach 1:
The self-attention mechanism provides multi-functionality by simultaneously capturing local features, global context, and long-range dependencies within a unified framework. This universal approach allows the model to handle diverse scenarios including simple scenes, complex environments, and cluttered settings effectively, improving reliability across varying operational conditions without requiring separate specialized models.
Data Source
Figure 1~2
AI summary
A method (20) for training a transformer model for semi-supervised image segmentation, the transformer model comprising a teacher model (T) and a student model (S), the method comprising: - obtaining a dataset of unlabeled images (Du) and inputting each unlabeled image into the teacher model (T) with a weak augmentation and into the student model (S) with a stronger augmentation; - generating a pseudo-label for the strongly augmented unlabeled image based on the weakly augmented unlabeled image using the teacher model (T), and using the pseudo-label to train the student model (S) comprising a self-attention mechanism incorporating a feature perturbation; and - aligning output of the student model (S) with the pseudo-label.