Region-based Fully Convolutional Networks

Region-based Fully Convolutional Networks（R-FCN）是一种目标检测算法，它基于全卷积网络来实现目标检测，可以在保持高精度的情况下加快检测速度。R-FCN的核心思想是将RoI（Region of Interest）池化操作替换成RoI对应的特征图上的卷积操作，从而避免了RoI池化操作中的信息损失和计算浪费。在R-FCN中，每个RoI都被转化为一个大小固定的特征图，然后通过卷积操作实现目标检测。R-FCN在目标检测任务中取得了较好的效果，尤其是在计算速度方面具有优势。

相关问题

region-cascade

region-cascade是一个支持设置行政编码和可选权限的中国行政区划级连下拉框。它是基于React开发的，可以通过打包成可在浏览器中执行的JS插件来使用。通过使用region convolution技术，region-cascade实现了深层级联的功能。此外，region-cascade还支持bounding box的回归，并在卷积层后增加了一个回归分支。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
#### 引用[.reference_title]
- *1* [region-cascade:中国行政区划级连下拉框，支持设置行政编码，设置可选权限](https://download.csdn.net/download/weixin_42127748/18855579)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v93^chatsearchT3_1"}}] [.reference_item style="max-width: 33.333333333333336%"]
- *2* [region-conv:并非所有像素均相等](https://download.csdn.net/download/weixin_42127783/18791599)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v93^chatsearchT3_1"}}] [.reference_item style="max-width: 33.333333333333336%"]
- *3* [论文阅读《R-FCN: Object Detection via Region-based Fully Convolutional Networks》](https://blog.csdn.net/AmbitionalH/article/details/120267888)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v93^chatsearchT3_1"}}] [.reference_item style="max-width: 33.333333333333336%"]
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Transformer-Based Visual Segmentation: A Survey

Visual segmentation is one of the most important tasks in computer vision, which involves dividing an image into multiple segments, each of which corresponds to a different object or region of interest in the image. In recent years, transformer-based methods have emerged as a promising approach for visual segmentation, leveraging the self-attention mechanism to capture long-range dependencies in the image.

This survey paper provides a comprehensive overview of transformer-based visual segmentation methods, covering their underlying principles, architecture, training strategies, and applications. The paper starts by introducing the basic concepts of visual segmentation and transformer-based models, followed by a discussion of the key challenges and opportunities in applying transformers to visual segmentation.

The paper then reviews the state-of-the-art transformer-based segmentation methods, including both fully transformer-based approaches and hybrid approaches that combine transformers with other techniques such as convolutional neural networks (CNNs). For each method, the paper provides a detailed description of its architecture and training strategy, as well as its performance on benchmark datasets.

Finally, the paper concludes with a discussion of the future directions of transformer-based visual segmentation, including potential improvements in model design, training methods, and applications. Overall, this survey paper provides a valuable resource for researchers and practitioners interested in the field of transformer-based visual segmentation.