Pre-Trained Object Detection Model Adaptation for New Classes
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Solution Overview
Problem
Existing object detection models face challenges in adding new object classes without forgetting old classes, leading to 'catastrophic forgetting', and conventional methods like full retraining are time-consuming and resource-intensive.
Innovation Solution
A technique that maintains the pre-trained model's architecture by using data augmentation and automatic labeling, combining original weights with new class weights, and applying adapters like LoRA or DoRA to add new classes efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If full retraining is performed to add new object classes, then the model can detect new classes, but the training time and computational resources increase significantly
Solution Approach 1:
The training process is segmented into two distinct phases: (1) pre-training on source domain data with source object classes, and (2) incremental training on target domain data with target object classes. This segmentation allows the model to first learn general features from source data, then adapt to target domain specifics without retraining from scratch, significantly reducing training time while maintaining ability to detect both source and target object classes
Solution Approach 2:
The model performs preliminary learning of general object features through pre-training on source domain data before encountering target domain data. This preliminary action establishes a solid feature foundation that accelerates subsequent adaptation to new object classes, eliminating the need to learn basic features again during full retraining
2Adaptability or versatility
If full retraining is performed to add new object classes, then the model can detect new classes, but computational resources and cost increase
Solution Approach 1:
The computational workload is segmented and distributed across two training stages. The pre-training stage processes source domain data to establish general features, while the incremental training stage processes only target domain data for adaptation. This segmentation reduces total computational resource consumption compared to full retraining, as the model leverages previously learned features rather than recomputing them
Solution Approach 2:
The model copies and transfers learned feature representations from the pre-training phase to the incremental training phase. Instead of重新 learning features during full retraining, the model copies existing knowledge and adapts it to new object classes, significantly reducing computational resource requirements
3Adaptability or versatility
If the model is trained with new object classes, then it can detect new classes, but performance on old classes deteriorates due to catastrophic forgetting
Solution Approach 1:
The model performs preliminary learning of robust general features from source domain data that are transferable to both source and target object classes. This preliminary action creates a stable feature foundation that resists catastrophic forgetting, allowing the model to adapt to new classes while maintaining performance on old classes through rather than overwriting learned representations
Solution Approach 2:
General object features learned during pre-training serve as an intermediary between source and target object classes. These intermediate features capture essential patterns that apply across different domains and classes, enabling the model to detect new classes while maintaining performance on old classes through the mediating role of transferable features
4Adaptability or versatility
If conventional training methods are used to add new classes, then new classes can be detected, but the process is not scalable to multiple new classes
Solution Approach 1:
The training process is segmented into a reusable pre-training phase followed by incremental adaptation phases. This segmentation creates a scalable framework where the same incremental training methodology can be applied repeatedly to add multiple new object classes without increasing process complexity, as each new class follows the established incremental training pattern
Solution Approach 2:
The incremental training approach serves multiple functions: it can add single new object classes, multiple new classes simultaneously, and adapt to different target domains. This multi-functionality makes the training process highly scalable and versatile, eliminating the need for different training procedures for different scenarios
Data Source
AI summary
Systems and methods are provided for implementing training of a pre-trained object detection model for detecting new object classes. In examples, to train an object detection model, which has been pre-trained with a first set of object classes, with a new object class, a computing system applies to each of a plurality of first images that each depicts an object corresponding to an object class among the first set of object classes, a set of data augmentations combining each first image with at least one second image among a plurality of second images that each depicts a second object corresponding to the new object class, to generate a plurality of augmented images. The computing system trains the object detection model using the plurality of augmented images. In examples, original weights corresponding to the first set of object classes are retained, while random weights are used for the new object class.