重新思考基于深度学习的多标签图像分类：分类法、挑战与展望
Rethinking Multi-Label Image Classification With Deep Learning: Taxonomy, Challenge, and Outlook
AI总结：
本文综述了基于深度学习的多标签图像分类方法，将其分为六类，并总结了关键挑战与未来研究方向。
摘要：
多标签图像分类（MLIC）是计算机视觉中的一项基本任务，专注于识别图像中的多个物体或概念，支撑着众多现实世界应用，如自动驾驶、疾病诊断、推荐系统和移动服务机器人。过去十年中，基于卷积神经网络、循环神经网络和Transformer的深度学习范式，凭借其在视觉表示和关系建模方面的强大能力，显著推动了该领域的发展。这些进步极大地提升了MLIC模型在多样化数据集和应用领域的鲁棒性、可扩展性和泛化能力。在本综述中，我们对基于深度学习的MLIC文献进行了全面回顾。具体而言，我们首先回顾了背景，包括问题定义、数据集、骨干网络和评估指标。接着，我们为基于深度学习的MLIC方法开发了一个合理的分类法，将其组织为六组：面向区域的方法、面向标签的方法、面向架构的方法、面向表示的方法、面向学习的方法和面向数据的方法。最后，我们深入阐述了MLIC中的潜在学习博弈及其对其他视觉领域的影响，并实证总结了MLIC中的关键挑战和研究方向，同时概述了未来发展的有前景的途径。我们相信，本综述为研究社区提供了关于MLIC的整体和系统视角，从而促进该领域及更广泛领域的后续探索和创新。
英文摘要：
Multi-label image classification (MLIC), a fundamental task in computer vision, focuses on identifying multiple objects or concepts within an image, underpinning numerous read-world applications, such as autonomous driving, disease diagnosis, recommendation system, and mobile service robot. Over the past decade, deep learning paradigms based on convolutional neural networks, recurrent neural networks, and Transformers have significantly advanced this field, owing to their powerful capability in visual representation and relationship modeling. These advances have markedly improved the robustness, scalability, and generalization ability of MLIC models across diverse datasets and application domains. In this survey, we provide a comprehensive review of the deep learning-based literature on MLIC. Concretely, we first revisit the background, including problem definition, datasets, backbones and evaluation metrics. Next, we develop a plausible taxonomy for the deep learning-based MLIC approaches, organizing them into six groups: region-oriented methods, label-oriented methods, architecture-oriented methods, representation-oriented methods, learning-oriented methods, and data-oriented methods. Finally, we provide an insightful exposition of the underlying learning game in MLIC and its implications for other vision domains, and we empirically summarize the key challenges and research directions in MLIC while outlining promising avenues for future development. We believe this survey offers the research community a holistic and systematic perspective on MLIC, thereby facilitating subsequent exploration and innovation in this field and beyond.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
