Swin

AI新星Swin Transformer：计算机视觉领域的“观察高手”

在人工智能飞速发展的今天，让机器“看懂”世界是科学家们孜孜不倦追求的目标。从识别一张图片中的猫狗，到自动驾驶汽车精确判断交通状况，计算机视觉（CV）技术正以前所未有的速度改变着我们的生活。在这场视觉革命中，Swin Transformer脱颖而出，被誉为是计算机视觉领域的一颗耀眼新星。它不仅在图像分类、目标检测和语义分割等任务上屡创佳绩，更被ICCV 2021评选为最佳论文，彰显了其颠覆性的创新价值。

那么，Swin Transformer究竟是什么？它为何如此强大？让我们用生活中的例子，一起揭开它的神秘面纱。

视觉AI的“进化史”：从局部到全局的探索

想象一下，你是一位经验丰富的画家，要描绘一幅宏大的山水画。

  1. 卷神经网络（CNN）：局部细节的“显微镜”
    早期，深度学习领域的“霸主”是卷积神经网络（CNN）。CNN就像一位擅长用“显微镜”观察细节的画家。它通过一层层卷积核，能出色地捕捉图像中的局部特征，比如物体的边缘、纹理等。CNN在处理图像局部信息方面效率很高，但在理解图像的整体结构和远程依赖关系时，却显得力不从心。这就像画家只专注于描绘一片叶子的脉络，却难以把握整棵树乃至整个山林的意境。

  2. Transformer与Vision Transformer (ViT)：“长远眼光”的局限
    后来，Transformer模型在自然语言处理（NLP）领域大放异彩，它以强大的“全局注意力”机制，能够理解句子中任意词语之间的关联，就像一位能读懂长篇巨著、把握角色命运走向的文学大师。科学家们受到启发，尝试将Transformer引入计算机视觉领域，诞生了Vision Transformer（ViT）。

    ViT的思路很直接：把图片像文字一样切分成一个个小块（称为Patch），然后让Transformer像处理句子中的“词语”一样处理这些Patch，捕捉它们之间的全局关系。这就像画家想从宏观上把握整幅山水画的构图。然而，图像的分辨率往往比文本长得多，一张高清图片可能拥有成千上万个Patch。如果每个Patch都要和所有其他Patch进行“对话”（即全局自注意力计算），那么计算量将呈平方级增长，耗时耗力，就像要在有限时间内，把巨著的每一个字都和所有其他字进行排列组合，几乎不可能完成。对于需要处理高分辨率图像和进行像素级密集预测的任务，ViT的计算开销变得难以承受。

Swin Transformer：局部与全局的巧妙融合

面对ViT的这一“甜蜜的烦恼”，微软亚洲研究院的研究人员们提出了Swin Transformer（Swin的含义是“Shifted Window”，即“移位窗口”），它成功地将Transformer的“长远眼光”与CNN的“局部专注”巧妙结合，既高效又强大。Swin Transformer的核心思想可以概括为两个关键创新：分层结构和移位窗口机制。

1. 分层结构：从宏观到微观的“望远镜”

Swin Transformer 没有像ViT那样一开始就把图像处理成单一尺度的Patch序列，而是借鉴了CNN的特点，采用了分层设计。这就像我们观察一副巨大的山水画：

  • 第一层（远处观察）：你可能先用肉眼看清画面的大致轮廓和主要景物（低分辨率、大感受野）。
  • 第二层（近一点看）：走近一些，开始注意到一些小桥流水、亭台楼阁（中分辨率、中等感受野）。
  • 第三、第四层（用放大镜看）：最后拿出放大镜，细致入微地观察每一笔墨的晕染、每一片树叶的形态（高分辨率、小感受野）。

Swin Transformer正是通过这种多尺度、分层递进的方式，逐步提取图像的特征。它会逐渐缩小特征图的分辨率，却同时增加通道数，从而形成一种金字塔状的特征表示。这种方式使得Swin Transformer能够灵活地处理不同尺寸的视觉物体，也能很好地应用于目标检测和语义分割等需要多尺度特征的任务。

2. 移位窗口：既分工又协作的“团队合作”

这是Swin Transformer最精髓、最巧妙的设计，也是它名字的由来。前面提到，直接进行全局自注意力计算成本太高。Swin Transformer借鉴了“团队合作”的思路：

  • 窗口注意力（W-MSA）：高效的“局部小组”
    想象你和一群同事要共同处理一百万张图片。如果每个人都独立地扫描所有图片，效率会很低。Swin Transformer的做法是，把一张大图分成多个固定大小的、互不重叠的“窗口”。每个“窗口”内的Patch只在彼此之间进行自注意力计算，就像把你的同事们分成若干个小团队，每个团队只负责处理自己被分配到的那一小部分图片。这样，每个团队内部的沟通（计算）效率就大大提高了，总的计算量也从平方级降低到了线性级。

    然而，这样做有一个显而易见的缺点：不同窗口之间的信息是隔绝的，就像各个团队之间互不交流，团队A不知道团队B正在处理的内容。这会导致模型难以捕捉跨窗口的全局信息。

  • 移位窗口（SW-MSA）：“轮班制”的信息共享
    为了解决不同窗口之间缺乏信息交流的问题，Swin Transformer引入了**“移位窗口”机制**。

    在模型处理的下一层，它会将这些“窗口”整体进行策略性地平移（通常是原来窗口大小的一半）。这就像第一轮观察结束后，所有小团队的工位整体向右和向下移动半格，重新划分工作区域。由于窗口位置的移动，一部分原本在不同窗口边缘的Patch现在被分到了同一个窗口中，从而可以在新的窗口内进行信息交换。

    通过在相邻的层中交替使用非移位窗口和移位窗口两种机制，Swin Transformer 成功实现了：

    • 计算效率高：自注意力计算被限制在局部窗口内，计算复杂度与图像尺寸呈线性关系。
    • 全局信息捕获：通过窗口的移位，有效地建立了不同窗口之间的联系，使得信息能够在整个图像中流动，从而捕捉到全局语境。 这就像两个团队轮流值班，通过交错的班次，确保了整个区域的所有角落都能被关注到，并且不同区域的信息能够互相传递，最终形成对整体的全面理解。

Swin Transformer的优势与应用

Swin Transformer凭借其独特的架构设计，展现出强大的性能和广泛的适用性：

  • 卓越的性能：在ImageNet、COCO和ADE20K等多个主流计算机视觉基准测试中，Swin Transformer在图像分类、目标检测和语义分割任务上超越了此前的最先进模型。
  • 高效且可扩展：其线性计算复杂度使其能够高效处理高分辨率图像，同时还能通过扩大模型规模进一步提升性能。
  • 通用骨干网络：Swin Transformer被设计为通用的视觉骨干网络，可以方便地集成到各种视觉任务中，为图像生成、视频动作识别、医学图像分析等领域提供了强大的基础模型。
  • 替代CNN的潜力：其成功突破了CNN长期在计算机视觉领域的主导地位，被认为是Transformer在CV领域通用化的重要里程碑，甚至可能成为CNN的完美替代方案。

最新进展与未来展望

Swin Transformer的成功激发了研究界对视觉大模型的探索。2021年末，微软亚洲研究院的研究员们进一步推出了Swin Transformer V2，将模型参数规模扩展到30亿，并在保持高分辨率任务性能的同时，解决了大模型训练的稳定性问题，再次刷新了多项基准记录。

Swin Transformer的出现，如同为计算机视觉领域带来了新的“观察高手”，它用巧妙的机制平衡了效率与效果，让AI能够更高效、更全面地理解我们眼中的世界。未来，我们期待Swin Transformer及其后续演进，能在更多实际应用中大放异彩，推动AI走向更广阔的征程。

AI Rising Star Swin Transformer: The “Observation Expert” in Computer Vision

In the rapid development of artificial intelligence today, enabling machines to “understand” the world is a goal that scientists tirelessly pursue. From actively identifying cats and dogs in a picture to autonomous vehicles accurately judging traffic conditions, Computer Vision (CV) technology is changing our lives at an unprecedented speed. In this visual revolution, Swin Transformer stands out and is hailed as a dazzling new star in the field of computer vision. It has not only achieved great success in tasks such as image classification, object detection, and semantic segmentation but was also selected as the Best Paper at ICCV 2021, highlighting its disruptive innovative value.

So, what exactly is Swin Transformer? Why is it so powerful? Let’s uncover its mystery with examples from daily life.

The “Evolutionary History” of Visual AI: Exploration from Local to Global

Imagine you are an experienced painter depicting a magnificent landscape painting.

  1. Convolutional Neural Network (CNN): The “Microscope” for Local Details
    In the early days, the “hegemon” of the deep learning field was the Convolutional Neural Network (CNN). CNN is like a painter who is good at using a “microscope” to observe details. Through layers of convolution kernels, it can excellently capture local features in images, such as edges and textures of objects. CNN is highly efficient in processing local image information, but it seems powerless in understanding the overall structure and long-range dependencies of images. This is like a painter focusing only on depicting the veins of a leaf but finding it difficult to grasp the artistic conception of the entire tree and even the whole forest.

  2. Transformer vs. Vision Transformer (ViT): Limitations of “Long-Term Vision”
    Later, the Transformer model shone in the field of Natural Language Processing (NLP). With its powerful “Global Attention” mechanism, it can understand the association between any words in a sentence, just like a literary master who can read long masterpieces and grasp the fate of characters. Scientists were inspired to try to introduce Transformers into the computer vision field, giving birth to Vision Transformer (ViT).

    ViT’s idea is direct: cut the image into small pieces (called Patches) like text, and then let the Transformer process these Patches like “words” in a sentence to capture the global relationship between them. This is like a painter trying to grasp the composition of the entire landscape painting from a macro perspective. However, the resolution of images is often much larger than text, and a high-definition picture may have thousands of patches. If every patch has to have a “dialogue” (i.e., global self-attention calculation) with all other patches, the calculation amount will grow quadratically. It takes time and effort, just like arranging and combining every word of a masterpiece with all other words in a limited time, which is almost impossible to complete. For tasks that require processing high-resolution images and performing pixel-level dense predictions, the computational overhead of ViT becomes unbearable.

Swin Transformer: The Ingenious Fusion of Local and Global

Facing this “sweet burden” of ViT, researchers at Microsoft Research Asia proposed Swin Transformer (Swin stands for “Shifted Window“). It successfully combines the “long-term vision” of Transformer with the “local focus” of CNN, making it both efficient and powerful. The core idea of Swin Transformer can be summarized into two key innovations: Hierarchical Structure and Shifted Window Mechanism.

1. Hierarchical Structure: The “Telescope” form Macro to Micro

Swin Transformer does not process the image into a single-scale patch sequence from the beginning like ViT, but draws on the characteristics of CNN and adopts a Hierarchical Design. This is like observing a huge landscape painting:

  • Layer 1 (Observation from a distance): You might first see the general outline and main scenery of the picture with the naked eye (Low resolution, large receptive field).
  • Layer 2 (Look closer): Get closer and start noticing some small bridges, flowing water, pavilions, and terraces (Medium resolution, medium receptive field).
  • Layers 3 & 4 (Look with a magnifying glass): Finally, take out a magnifying glass to observe the shading of every stroke and the shape of every leaf in detail (High resolution, small receptive field).

Swin Transformer extracts image features step by step through this multi-scale, hierarchical progression. It gradually reduces the resolution of the feature map while increasing the number of channels, thus forming a pyramid-like feature representation. This method allows Swin Transformer to flexibly handle visual objects of different sizes and is also well utilized in multi-scale feature tasks such as object detection and semantic segmentation.

2. Shifted Window: “Teamwork” with Division of Labor and Collaboration

This is the most essential and ingenious design of Swin Transformer, and also the origin of its name. As mentioned earlier, direct global self-attention calculation is too costly. Swin Transformer borrows the idea of “teamwork”:

  • Window Attention (W-MSA): Efficient “Local Group”
    Imagine you and a group of colleagues have to process one million pictures together. If everyone scans all pictures independently, efficiency will be very low. Swin Transformer’s approach is to divide a large picture into multiple fixed-size, non-overlapping “windows”. The patches within each “window” only perform self-attention calculations with each other, just like dividing your colleagues into several small teams, with each team only responsible for processing the small part of the pictures assigned to them. In this way, the communication (calculation) efficiency within each team is greatly improved, and the total calculation amount is reduced from quadratic to linear.

    However, there is an obvious disadvantage to doing this: information between different windows is isolated, just like teams not communicating with each other—Team A doesn’t know what Team B is processing. This makes it difficult for the model to capture cross-window global information.

  • Shifted Window (SW-MSA): “Shift Rotation” for Information Sharing
    To solve the problem of lack of information exchange between different windows, Swin Transformer introduced the “Shifted Window” mechanism.

    In the next layer processed by the model, it will strategically shift these “windows” as a whole (usually by half the size of the original window). This is like after the first round of observation, all small teams’ workstations are shifted to the right and down by half a grid as a whole, re-dividing the work area. Due to the movement of the window position, patches originally at the edges of different windows are now assigned to the same window, allowing information exchange within the new window.

    By alternating the use of non-shifted window and shifted window mechanisms in adjacent layers, Swin Transformer successfully achieves:

    • High Computational Efficiency: Self-attention calculation is limited to local windows, and computational complexity is linear with image size.
    • Global Information Capture: Through window shifting, connections between different windows are effectively established, allowing information to flow throughout the image, thereby capturing global context. This is like two teams taking turns on duty, ensuring through staggered shifts that every corner of the entire area is noticed, and information from different areas can be transmitted to each other, ultimately forming a comprehensive understanding of the whole.

Advantages and Applications of Swin Transformer

With its unique architectural design, Swin Transformer demonstrates powerful performance and broad applicability:

  • Outstanding Performance: In multiple mainstream computer vision benchmarks such as ImageNet, COCO, and ADE20K, Swin Transformer surpassed previous state-of-the-art models in image classification, object detection, and semantic segmentation tasks.
  • Efficient and Scalable: Its linear computational complexity allows it to efficiently process high-resolution images while further improving performance by expanding model scale.
  • General Backbone: Swin Transformer is designated as a general-purpose visual backbone network that can be easily integrated into various visual tasks, providing a powerful foundation model for fields such as image generation, video action recognition, and medical image analysis.
  • Potential to Replace CNN: Its success has broken the long-term dominance of CNNs in the field of computer vision and is considered an important milestone for the generalization of Transformers in the CV field, potentially becoming a perfect alternative to CNNs.

Latest Progress and Future Outlook

The success of Swin Transformer has inspired the research community to explore large vision models. In late 2021, researchers at Microsoft Research Asia further launched Swin Transformer V2, expanding the model parameter scale to 3 billion. While maintaining high performance on high-resolution tasks, it solved the stability problem of large model training and once again refreshed multiple benchmark records.

The emergence of Swin Transformer is like bringing a new “Observation Expert” to the field of computer vision. It balances efficiency and effectiveness with ingenious mechanisms, allowing AI to understand the world in our eyes more efficiently and comprehensively. In the future, we expect Swin Transformer and its subsequent evolutions to shine in more practical applications and drive AI towards a broader journey.