Overview of Pyramid Pooling Module
In the world of computer vision, semantic segmentation involves labeling every pixel in an image with a corresponding category. As such, it is a challenging task that requires a lot of computation. Convolutional neural networks like ResNet have proven to be effective in tackling the problem, but they still have their own limitations that need to be addressed. One of these limitations is the small empirical receptive field on high-level layers, which makes it difficult for the network to interpret global scenery.
The Problem with Convolutional Networks
Convolutional networks like ResNet have a larger receptive field compared to the input image, but their empirical receptive field is much smaller than their theoretical one, particularly on high-level layers. This empirical receptive field refers to the actual region in the input image that influences the prediction of a given output, while the theoretical one is the maximum region that can affect the output. As a result, convolutional networks may not incorporate the necessary information needed to understand the relationship between objects in the global scenery, which is crucial in semantic segmentation.
The Solution: Pyramid Pooling Module
To address this limitation, a Pyramid Pooling Module (PPM) was introduced as an effective global prior representation that could incorporate information with different scales and varying among different sub-regions. The PPM consists of a 4-level pyramid, where the pooling kernels cover the whole, half of, and small portions of the image, and they are fused as the global prior. The final feature map is then concatenated with the global prior.
With the PPM, the network is able to understand the global context of the scene, which is important in semantic segmentation. The PPM helps the network to identify objects in the image and their relationship with each other, leading to better segmentation results.
Benefits of Pyramid Pooling Module
The PPM offers several benefits that make it popular in the field of computer vision. One of the main benefits is that it provides a way for convolutional networks to incorporate global context, which is important in semantic segmentation. The PPM also helps to reduce over-fitting by pooling features from multiple regions of the image.
Another benefit of the PPM is its efficiency. Since the pooling kernels operate on different scales and sub-regions, the PPM is able to generate a representation with less computation than other methods. This means that the PPM is faster and more efficient than other algorithms, making it ideal for real-time applications that require quick inference times.
The Pyramid Pooling Module is a powerful tool in semantic segmentation, providing a way for convolutional networks to incorporate global context into their predictions. With its efficiency and ability to reduce over-fitting, the PPM has become a popular algorithm in the field of computer vision. As researchers continue to explore and develop new algorithms, the PPM will undoubtedly continue to play an important role in computer vision research and development.
