Object Detector Training Using Self-Supervised Embedding Feedback
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Solution Overview
Problem
Current object detection methods require large amounts of manually labeled data, which are costly, time-consuming, and limited in scope, leading to biased and inaccurate results due to human labeling and limited dataset coverage.
Innovation Solution
A self-supervised learning approach using a trained embedding neural network and exemplar-based labeling, where cropped images are used to train an object detector without the need for extensive manual labeling, optimizing the detector through gradients computed in embedding space.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If supervised deep learning approaches are used for object detection, then detection accuracy can be improved, but the cost and time of manual data labeling increase significantly
Solution Approach 1:
The system performs self-supervised learning by automatically generating labels from the data itself through contrastive learning. The embedding neural network learns to distinguish between different image crops without human intervention, allowing the model to train on unlabeled data while maintaining detection accuracy.
Solution Approach 2:
The method pre-trains an embedding neural network on unlabeled data using self-supervised contrastive learning before fine-tuning on labeled data. This preliminary action on unlabeled data prepares the model for more effective training with limited labeled examples, reducing the overall labeling time required.
2Measurement precision
If large manually labelled datasets are collected, then detection performance improves, but the cost of data collection and labeling increases
Solution Approach 1:
The system enables self-supervised pre-training that allows the model to learn from large amounts of unlabeled data automatically. This eliminates the need for expensive manual labeling of large datasets while still achieving good detection performance through self-learned representations.
Solution Approach 2:
The method creates synthetic training examples by generating various crops and augmentations from limited labeled images. These copied and transformed examples serve as additional training data without requiring actual collection and labeling of new images, reducing data labeling costs.
3Loss of information
If manual labeling is performed by human annotators, then labeled data can be obtained, but human bias is introduced into the detection system
Solution Approach 1:
The system replaces human annotators with an automated self-supervised learning process. The embedding neural network automatically learns distinguishing features from unlabeled data without human intervention, eliminating human visual system bias while still obtaining sufficient labeled information for training.
4Quantity of substance
If limited labeled data is used due to budget constraints, then project cost is reduced, but detection accuracy deteriorates
Solution Approach 1:
The method performs self-supervised pre-training on unlabeled data as a preliminary step before fine-tuning with limited labeled data. This preliminary learning of robust representations from abundant unlabeled data allows the model to achieve good detection accuracy even with very limited labeled examples, overcoming budget constraints.
Solution Approach 2:
The system generates multiple augmented versions and crops from limited labeled images to create expanded training sets. By copying and transforming the available labeled data through various augmentations, the effective training data amount increases without additional labeling costs, maintaining detection accuracy.
Data Source
AI summary
A computer-implemented method of training an object detector, the method comprising: training an embedding neural network using, as an input, cropped images from an image dataset, wherein training the embedding neural network is performed using a self-supervised learning approach and the trained embedding neural network translates input images into a lower dimensional representation; and training an object detector neural network by, for images of the image dataset, repeatedly: passing an image through the object detector neural network to obtain proposed coordinates of an object within the image, cropping the image to the proposed coordinates to obtain a cropped image, passing the cropped image through the trained embedding neural network to obtain a cropped image representation, passing an exemplar through the trained embedding neural network to obtain an exemplar representation, wherein the exemplar is a cropped manually labelled image bounding a known object, computing a distance in embedding space between the cropped image representation and the exemplar representation, computing a gradient of the cropped image representation and the exemplar representation with respect to the distance, and passing the gradient into the object detector neural network for use in backpropagation to optimise the object detector neural network.