Model Adaptive Learning: An Approach for Improving Object Recognition Efficiency - Trần Diễm Phúc (Journal of Technical Education Science, 2024)
In recent years, research on artificial intelligence (AI) has experienced remarkable advancements. Many practical applications are the result of ongoing research, such as recognition technology, self-driving cars, translation, etc and most recently, the explosion of some AI-powered intelligent chatbots using large language models. Major corporations and research institutions worldwide are racing to develop AI models capable of the most accurate interaction based on user requests. However, despite achieving certain milestones, current AI models still fall short of the required intelligence to function similarly to the human brain. Based on research and experimentation, we propose an adaptive learning model that allows models to continuously learn during operation, select, and store previously acquired experiential knowledge to serve on-demand tasks. The proposed solution consists of four steps: (1) Initializing the initial recognition model; (2) Detection, recognition and collecting data from various instances of objects during operation based on object tracking; (3) Searching, selecting optimal models and hyperparameters on the discovered dataset; (4) Training and updating the model. The results of the proposed research could be a promising direction for the development of an adaptive learning model in advanced object recognition.