Image segmentation, a key task in computer vision, has traditionally relied on convolutional neural networks (CNNs), yet these models struggle with capturing complex spatial dependencies, objects with varying scales, need for manually crafted architecture components and contextual information. This paper explores the shortcomings of CNN-based models and the shift towards transformer architectures -to overcome those limitations. This work reviews state-of-the-art transformer-based segmentation models, addressing segmentation-specific challenges and their solutions. The paper discusses current challenges in transformer-based segmentation and outlines promising future trends, such as lightweight architectures and enhanced data efficiency. This survey serves as a guide for understanding the impact of transformers in advancing segmentation capabilities and overcoming the limitations of traditional models.

翻译：图像分割作为计算机视觉的核心任务，传统上依赖于卷积神经网络（CNN），然而这些模型在捕捉复杂空间依赖关系、处理多尺度目标、避免手工设计架构组件以及整合上下文信息方面存在局限。本文系统探讨了基于CNN模型的固有缺陷，并阐述了向Transformer架构转型以突破这些限制的趋势。本工作全面回顾了基于Transformer的先进分割模型，针对分割任务特有的挑战及其解决方案进行了深入分析。论文进一步讨论了当前Transformer分割技术面临的挑战，并展望了具有前景的未来发展方向，例如轻量化架构设计与数据效率提升。本综述旨在为理解Transformer在提升分割能力、突破传统模型局限方面的影响提供系统性指引。