When it comes to object detection in computer vision, the Scale-wise Feature Aggregation Module, or SFAM, has emerged as a critical component of many state-of-the-art neural network architectures. SFAM is a feature extraction block that aims to aggregate multi-level multi-scale features into a multi-level feature pyramid. This allows the neural network to detect objects of different sizes and scales, which is especially important in applications like autonomous driving and robotics.
What is SFAM?
SFAM is a key building block in the M2Det architecture, which has achieved state-of-the-art performance on benchmarks like COCO and PASCAL VOC. The goal of SFAM is to combine the multi-level multi-scale features generated by Thinned U-Shaped Modules into a multi-level feature pyramid. This feature pyramid provides the neural network with representations of the image at different scales, which can be used to detect objects of different sizes.
In the first stage of SFAM, features of the equivalent scale are concatenated together along the channel dimension. This creates an aggregated feature pyramid that can be presented as a matrix where each row represents a particular scale in the pyramid and each column represents a feature. Each scale in the pyramid consists of features from multiple levels of the neural network.
How Does SFAM Work?
While simple concatenation operations can be useful, they are not always adaptive enough. To address this issue, SFAM introduces a channel-wise attention module. This module encourages features to focus on the channels that benefit them the most. The channel-wise attention module uses global average pooling to generate channel-wise statistics, which it then uses to learn the attention mechanism.
The attention mechanism is built using two fully connected layers. The first layer reduces the number of channels in the feature map by a factor of r, which is typically set to 16. The second layer restores the original number of channels in the feature map. The output of the attention mechanism is a set of weights that can be used to rescale the features in the feature map.
After the attention mechanism has been applied, the final output is obtained by reweighting the input with the activation. Each feature is then enhanced or weakened by the rescaling operation.
Why is SFAM Important?
SFAM is critical for achieving state-of-the-art performance in object detection. This is because it allows neural networks to detect objects of different sizes and scales. Without SFAM, neural networks would struggle to detect smaller objects and would be limited to detecting only larger objects.
In addition to its importance in object detection, SFAM has also been used in other computer vision applications like semantic segmentation and image classification. In each of these applications, SFAM has helped neural networks to perform better by providing them with representations of the image at different scales.
SFAM is a key building block in many state-of-the-art neural network architectures. By aggregating multi-level multi-scale features into a multi-level feature pyramid, SFAM allows neural networks to detect objects of different sizes and scales. This is a critical feature for many computer vision applications like autonomous driving and robotics, where the ability to detect objects of different sizes is essential. In addition to its importance in object detection, SFAM has also been used in other computer vision applications like semantic segmentation and image classification.
