To automatically update and expand the versatile pedestrian knowledge bank as new scene data becomes available, the proposed method can be extended by implementing a continual learning approach. This approach involves incorporating mechanisms for incremental learning and knowledge retention. Here are some key steps to achieve this:
Incremental Learning: Implement a system that can incrementally update the knowledge bank with new pedestrian instances as they become available. This involves continuously feeding new data into the system, extracting generalized pedestrian embeddings, and curating them to update the existing knowledge bank.
Knowledge Retention: Develop strategies to retain valuable knowledge while incorporating new data. This can involve techniques such as knowledge distillation, where the existing knowledge is distilled into a compact form and merged with new knowledge to update the knowledge bank without losing important information.
Adaptive Quantization: Incorporate adaptive quantization techniques that can dynamically adjust the quantized representations in the knowledge bank based on the relevance and importance of new data. This ensures that the knowledge bank remains up-to-date and relevant to the evolving scene data.
Regular Evaluation and Validation: Implement a system for regular evaluation and validation of the knowledge bank to ensure that the updated representations are effective in enhancing pedestrian detection performance. This feedback loop helps in refining the knowledge bank over time.
By incorporating these strategies, the proposed method can be extended to automatically update and expand the versatile pedestrian knowledge bank with new scene data, ensuring its relevance and effectiveness in diverse scenarios.