Accurate Semantic Image Segmentation Using Attention Mechanism

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已于 2023-10-09 02:26:02 修改

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分类专栏： AI大模型企业级应用开发实战文章标签：大数据人工智能语言模型 Java Python 架构设计

于 2023-10-09 02:24:43 首次发布

本文链接：https://blog.youkuaiyun.com/universsky2015/article/details/133692939

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本文提出了一种基于注意力的全卷积网络（ABFCN）架构，结合条件随机场（CRF）后处理，用于从RGB图像中准确地进行语义区域分割。该方法使用ResNet50作为基础，通过注意力机制关注不同尺度的重要特征，并使用DC-CRF进行分割掩模的细化。实验证明，该方法在精度上优于现有算法。

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作者：禅与计算机程序设计艺术

1.背景介绍

Semantic image segmentation (SIs) is one of the most challenging tasks in computer vision and medical imaging fields due to its high variability and complexity of realistic scenes with complex structures and textures. In this paper, we present an attention-based fully convolutional network (ABFCN) architecture that leverages a deep learning technique called conditional random field (CRF) post-processing to segment semantic regions accurately from RGB images. The proposed approach first uses a feature extractor to extract feature maps at multiple scales for both foreground and background objects using resnet50 as backbone. Then, we apply an attention mechanism to selectively focus on important features at different scales. Ne