面向目标分类识别的多任务学习算法综述

Multi-Task Learning(MTL) aims to enhance the model performance by jointly leveraging supervisory signals and sharing useful information among multiple related tasks. This paper comprehensively summarizes and analyzes the mechanism and mainstream methods of multi-task learning for object classification and recognition applications. First, we review the definitions, principles and methods of MTL. Second, taking the representative and widely used fine-grained classification and object re-identification as examples, we emphatically introduce two types of multi-task learning for object classification and recognition: task-based multi-task learning and feature-based multi-task learning, and further categorize each type and analyze the design ideas, and advantages and disadvantages of different MTL algorithms. Third, we compare the performance of various MTL algorithms reviewed in this paper on common datasets. Finally, prospects on development trends of MTL algorithms for object classification and recognition are discussed.