Overview of Self-Calibrated Convolutions
Self-calibrated convolution is a technique used to enlarge the receptive field of a neural network by improving its adaptability. This breakthrough technique was developed by Liu et al. and has shown impressive results in image classification and other visual perception tasks such as keypoint and object detection.
What is a Convolution?
Before delving into self-calibrated convolutions, it is important to understand what a convolution is in the context of neural networks.
A convolution is a mathematical operation that involves merging two functions to get a third function that contains information from both. In the context of neural networks, this involves applying a filter to different parts of an image to extract features that can be used for classification or other tasks.
How Self-Calibrated Convolutions Work
Self-calibrated convolutions work by dividing the input feature map into two parts - $X_{1}$ and $X_{2}$ - along the channel dimension.
The self-calibrated convolution begins by using an average pooling operation to reduce the size of $X_{1}$ and improve its receptive field. This operation is defined as:
T1 = AvgPoolr(X1)
Where r is the filter size and stride. The resulting feature map is then passed through a convolution layer and a bilinear interpolation operator to upsample the feature map. This is defined as:
X'1 = Up(Conv2(T1))
Using the element-wise multiplication, the self-calibrated process is finished:
Y'1 = Conv3(X1) σ(X1 + X'1)
The result is finally passed through another convolution operation:
Y1 = Conv4(Y'1)
In parallel, X2 is passed through a simple convolution operation:
Y2 = Conv1(X2)
The final output feature map is then formed:
Y = [Y1; Y2]
Benefits of Self-Calibrated Convolutions
The self-calibrated convolution technique achieves excellent results in various image classification tasks as well as other downstream tasks such as object detection and keypoint detection. The main benefit of this technique is that it improves the adaptability of a neural network by enlarging its receptive field, which allows it to better recognize and classify objects in different settings.
Self-calibrated convolutions are a powerful technique for improving the accuracy and adaptability of neural networks in various visual perception tasks. By dividing the input feature map into two parts and passing it through a series of convolution and pooling layers, self-calibrated convolutions can enlarge the receptive field and provide more accurate classification results.
