Computer Vision Tasks | Detection, Segmentation, and Model Visualization | 计算机视觉任务与可解释性 скачать в хорошем качестве

Computer Vision Tasks | Detection, Segmentation, and Model Visualization | 计算机视觉任务与可解释性

This lecture provides a structured overview of core computer vision tasks and how we interpret and understand deep vision models. We begin with fundamental vision problems, including object detection, semantic segmentation, and instance segmentation. We then move beyond task performance and explore visualization and interpretability techniques that help us understand model behavior. This lecture emphasizes conceptual understanding and structural intuition rather than implementation details. Topics covered: 1. Object Detection: localization and classification 2. Semantic vs. Instance Segmentation 3. Model layer visualization 4. Saliency maps 5. CAM and Grad-CAM ================================ 在本讲中，我们系统梳理计算机视觉中的核心任务，以及如何理解与解释深度视觉模型。 我们首先介绍目标检测、语义分割与实例分割等基本任务，随后进一步探讨模型可视化与可解释性方法，包括模型层可视化、Saliency Map 以及 CAM / Grad-CAM。 本讲重点在于结构理解与思想建立，而非代码实现。 本讲内容包括： 1. 目标检测：定位与分类 2. 语义分割与实例分割的区别 3. 模型层可视化 4. 显著性图（Saliency Map） 5. CAM 与 Grad-CAM