deformable roi pooling

可变形ROI池化是一种用于目标检测的技术，它可以处理不同大小和形状的目标。它通过对感兴趣区域（ROI）进行可变形变换来适应目标的形状，然后将变换后的ROI划分为固定大小的网格，每个网格内的特征值通过池化操作得到一个固定大小的特征向量。这种技术可以提高目标检测的准确性和鲁棒性。

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Deformable Attention

Deformable Attention（可变形注意力）是一种处理输入数据条件下的空间位置的灵活机制。它最早在商汤研究院的《Deformable DETR: Deformable Transformers for End-to-End Object Detection》论文中被提出，并在后续的《Vision Transformer with Deformable Attention》中应用于通用视觉Transformer骨干网络DAT（Deformable Attention Transformer）。这一机制的效果在多个数据集上优于swin transformer。Deformable Attention通过为CNN骨干网顶部的每个查询选择少量键来提高DETR的收敛性。它还提供了一种简单而强大的设计，可以学习一组全局键，这些键在各种视觉任务中可以被共享，从而成为一种适用于不同视觉任务的通用主干。

deformable onnx

Deformable ONNX refers to a version of the Open Neural Network ExchangeONNX) format that support for deformable convolutional neural networks (DCNNs). Deformable convolutions are a type of convolutional layer used in computer vision tasks, which allow for more flexible and accurate feature extraction by allowing the receptive field of the convolutional filters to be adjusted based on the input data.

To support deformable convolutions in ONNX, additional operators and attributes are added to the standard ONNX specification. These include the DeformableConv operator, which performs the deformable convolution operation, as well as attributes that specify the parameters of the deformable convolution such as the number of output channels and the kernel size.

With support for deformable convolutions in ONNX, models that use these types of layers can be exported and used across different deep learning frameworks and hardware platforms that support the ONNX format. This allows for greater flexibility and portability of DCNN models, which can be particularly useful in scenarios where real-time inference is required or where hardware constraints limit the use of certain deep learning frameworks.