计算机视觉中局部机制的最新进展：近期工作综述与展望
Recent Advances of Local Mechanisms in Computer Vision: A Survey and Outlook of Recent Work
AI总结：
本文系统综述了面向细粒度识别、重识别、少/零样本、多模态、自监督学习及Vision Transformers等任务的计算机视觉局部机制，归纳分类、分析优劣并展望方向。
摘要：
受人类大脑能够强调输入中具有判别性的部分并抑制无关部分这一事实启发，大量局部机制已被设计出来以推动计算机视觉的发展。它们不仅能够聚焦目标部分以学习具有判别性的局部表示，还能够选择性地处理信息以提高效率。就应用场景和范式而言，局部机制具有不同的特征。在本综述中，我们系统回顾了面向各类计算机视觉任务和方法的局部机制，包括细粒度视觉识别、行人重识别、少样本/零样本学习、多模态学习、自监督学习、Vision Transformers等。文中总结了每个领域中局部机制的分类。随后，深入分析了每一类别的优缺点，并指出了尚待探索的空间。最后，还讨论了局部机制的未来研究方向，这些方向或可使未来工作受益。据我们所知，这是首篇关于计算机视觉局部机制的综述。我们希望本综述能够为计算机视觉领域的未来研究提供启发。
英文摘要：
Inspired by the fact that human brains can emphasize discriminative parts of the input and suppress irrelevant ones, substantial local mechanisms have been designed to boost the development of computer vision. They can not only focus on target parts to learn discriminative local representations, but also process information selectively to improve the efficiency. In terms of application scenarios and paradigms, local mechanisms have different characteristics. In this survey, we provide a systematic review of local mechanisms for various computer vision tasks and approaches, including fine-grained visual recognition, person re-identification, few-/zero-shot learning, multi-modal learning, self-supervised learning, Vision Transformers, and so on. Categorization of local mechanisms in each field is summarized. Then, advantages and disadvantages for every category are analyzed deeply, leaving room for exploration. Finally, future research directions about local mechanisms have also been discussed that may benefit future works. To the best our knowledge, this is the first survey about local mechanisms on computer vision. We hope that this survey can shed light on future research in the computer vision field.
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。
