2021 - PAConv Position Adaptive Convolution With Dynamic
时间: 2024-06-03 14:08:04 浏览: 14
Kernel Asymmetry for Object Detection
Object detection is a fundamental task in computer vision that involves identifying and localizing objects within an image. Recently, convolutional neural networks (CNNs) have achieved state-of-the-art performance in object detection. However, CNNs have a fixed receptive field and are not able to adapt to objects of different scales and shapes. To address this issue, a new type of convolutional layer called Position Adaptive Convolution (PAConv) has been proposed.
PAConv is a variant of the standard convolutional layer that can adapt its kernel size and shape according to the position of the input feature map. This allows PAConv to learn features that are more specific to the object being detected. Moreover, PAConv is able to handle objects of different scales and shapes by dynamically adjusting its kernel asymmetry.
The key idea behind PAConv is to divide the input feature map into a set of patches and apply a convolutional kernel to each patch. The size and shape of the kernel are determined by the position of the patch within the feature map. This allows PAConv to capture local features that are specific to the object being detected.
To further improve the performance of PAConv, a dynamic kernel asymmetry mechanism is introduced. This mechanism allows the kernel to be asymmetric in order to better capture the features of objects with different scales and shapes. The kernel asymmetry is determined by the position of the patch within the feature map.
Experimental results show that PAConv outperforms standard convolutional layers in object detection tasks. PAConv is able to achieve a higher mean average precision (mAP) score on the COCO dataset, a widely used benchmark for object detection. Moreover, PAConv is able to handle objects of different scales and shapes, making it a promising approach for real-world applications.
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