Person Re-Identification Model Using Memory Features and Pseudo-Labels
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current person re-identification methods face challenges in learning discriminative features without labeled data, are affected by domain gaps, and lack flexibility, leading to performance issues and high annotation costs.
Innovation Solution
A method and system for person re-identification that involves determining single-class labels and memory features, calculating multi-class labels through positive label prediction, and updating a model using a multi-label classification loss, which helps in training a robust re-identification model unaffected by domain gaps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If supervised learning with labeled person images is used, then re-identification performance is improved, but annotation cost increases
Solution Approach 1:
The system performs self-service by automatically generating pseudo-labels through clustering algorithms and iterative training without requiring external human annotation. The model trains itself using unlabeled data, eliminating the need for expensive manual labeling while maintaining performance through self-supervised learning mechanisms.
Solution Approach 2:
Clustering algorithms serve as an intermediary between unlabeled data and the training process. The clustering module generates pseudo-labels that bridge the gap between unlabeled images and the supervised learning framework, enabling the model to learn from unlabeled data while maintaining the benefits of supervised training approaches.
2Reliability
If transfer learning with labeled data from other domains is used, then model initialization is improved, but domain gap affects performance
Solution Approach 1:
The method extracts and removes the domain-specific labeled data requirement from the transfer learning process. By taking out the dependency on labeled source domain data and replacing it with unsupervised clustering-based pseudo-labeling, the system eliminates the negative impact of domain gaps while retaining the benefits of transfer learning for feature initialization.
3Device complexity
If hand-crafted features are used, then feature extraction is simplified, but robustness and discriminative power deteriorate
Solution Approach 1:
The system replaces manual hand-crafted feature extraction with automated deep learning-based feature extraction. Convolutional neural networks automatically learn robust and discriminative features from raw images, substituting the mechanical process of manual feature engineering with an adaptive, data-driven approach that produces more robust features.
4Quantity of substance
If clustering estimated pseudo-labels are used, then labeled data requirement is reduced, but pre-trained model quality becomes critical
Solution Approach 1:
The system performs preliminary clustering-based pseudo-labeling on unlabeled data before the main training process. This preliminary action creates initial labels that guide the subsequent training, allowing the model to learn from unlabeled data without requiring high-quality pre-trained models, as the pseudo-labels provide sufficient initial structure for learning.
Data Source
AI summary
The present application discloses a method and a system for person re-identification, the method including: inputting a training set to a model-to-be-trained, and determining a single-class label and memory features of each image data in the training set; determining multi-class labels through positive label prediction according to the single-class labels and a memory feature set; determining classification scores according to image features of each image data in the training set and the memory feature set; determining a multi-label classification loss according to the multi-class labels and the classification scores; and updating and training the model-to-be-trained to obtain a re-identification model according to the multi-label classification loss. The classification scores are determined according to the image features of each image data in the training set and the memory feature set, which is not affected by the domain gap; the multi-class labels are determined through positive label prediction according to the single-class labels and the memory feature set; then, the multi-label classification loss is determined according to the multi-class labels and the classification scores, and the model-to-be-trained is updated and trained, so that the resulting re-identification model has high performance, strong robustness and low cost.