Object Detection Cross-Domain Mixing for Unlabeled Domain Shift
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Solution Overview
Problem
Deploying object detection machine learning models at scale is challenging due to large domain shifts between applications and use cases, such as changes in lighting, weather, and location, making it difficult to generalize across disparate domains without scalable collection of target domain labels.
Innovation Solution
Implementing unsupervised domain adaptation (UDA) using a cross-domain mixing approach with a mixed-domain teacher framework, involving weak and strong augmentations, intra- and inter-domain mixing, and joint training with a teacher-student model, leveraging pseudo labels and adversarial learning to reduce domain gaps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If additional ground truth labels of target domain are collected, then cross-domain learning performance is improved, but scalability is reduced
Solution Approach 1:
The system performs self-service by generating pseudo-labels automatically through the teacher model without requiring external annotation resources. The teacher-student framework enables the system to self-train on target domain data by having the teacher generate labels that the student then learns from, eliminating the need for manual target domain labeling while maintaining cross-domain performance.
Solution Approach 2:
The teacher model serves as an intermediary between the source domain labeled data and the target domain unlabeled data. It bridges the domain gap by generating pseudo-labels that mediate the learning process, allowing the student model to learn from target domain data without direct access to ground truth labels, thus maintaining scalability.
2Reliability
If strong augmentation is applied to reduce domain gap, then domain adaptation performance is improved, but pseudo label noise increases
Solution Approach 1:
The system applies partial strong augmentation by using it only on source domain images that are then mixed with target domain images, rather than applying strong augmentation to all target domain images. This selective application reduces the introduction of noise while still providing the benefits of domain adaptation through the mixed augmentation approach in inter-domain mixes.
3Adaptability or versatility
If domain-invariant features are learned, then generalization across domains is improved, but training complexity increases
Solution Approach 1:
The system merges multiple training objectives into a unified training framework. It combines supervised learning from source domain labels, unsupervised learning from target domain pseudo-labels, and domain adaptation through inter-domain mixing into a single joint training process. This integration reduces training complexity compared to implementing separate training stages while achieving domain-invariant feature learning.
Data Source
AI summary
Implementations are described herein for improving unsupervised domain adaptation (UDA) by using improved adaptive teacher for object detection with cross-domain mix-up. In various implementations, cross-domain training of an object detection machine learning model may include: performing weak augmentation on images from a target domain DT to generate a first set of weakly augmented target domain images; perform strong augmentation on images from the source domain DS and images from the target domain DT to generate a second set of strongly augmented images; processing the second set of strongly augmented images to generate a third set of inter-domain mixes of the images from DS and DT; and jointly train the object detection machine learning model, as a student machine learning model, with a teacher machine learning model using the first and third sets.