Overview of PSANet
PSANet is a semantic segmentation architecture that utilizes a Point-wise Spatial Attention (PSA) module to aggregate long-range contextual information. It was designed to assist in the prediction of complex scenes by collecting information from nearby and faraway positions in the feature map.
PSANet is flexible and adaptive because each position in the feature map is connected with all other positions through self-adaptively predicted attention maps, allowing it to harvest
Overview of PSPNet – Semantic Segmentation Model
PSPNet, or Pyramid Scene Parsing Network, is a powerful semantic segmentation model that utilizes a pyramid parsing module to gather global context information through different-region based context aggregation. The aim of this model is to make the final prediction more reliable by combining local and global clues.
How PSPNet Works
When an input image is given to the PSPNet, it uses a pre-trained Convolutional Neural Network (CNN) with the dil
SegFormer: A Transformer-Based Framework for Semantic Segmentation
SegFormer is a newer approach for semantic segmentation, which refers to the process of dividing an image into different objects or regions and assigning each of those regions a label. This process is critical for a variety of tasks, such as machine vision and autonomous vehicles. SegFormer is based on a type of neural network architecture known as a Transformer, which has revolutionized natural language processing.
The Transf
Overview of SETR: A Transformer-Based Segmentation Model
SETR, which stands for Segmentation Transformer, is a cutting-edge segmentation model that is based on Transformers. As a category, Transformers are a versatile and powerful class of machine learning models that can be used for a variety of tasks, such as natural language processing and image recognition. In the context of SETR, the Transformer model is used as an encoder for segmentation tasks in computer vision.
By treating an input im
What is SegNet?
If you are interested in computer vision, then you might have heard of SegNet. It is a semantic segmentation model that is used to analyze images with great accuracy. SegNet consists of an encoder network that processes the input image and a decoder network that predicts the output.
How does SegNet work?
SegNet uses an encoder and a decoder network that work together to produce the desired output image. The encoder network processes the input image and produces low-resolution
U-Net: A Revolutionary Architecture for Semantic Segmentation
Understanding images and extracting various objects from them is an essential task in the field of computer vision. This is where semantic segmentation comes into play. It involves annotating each pixel from an image with a class label which represents the object it belongs to. But, manually labeling pixels is a time-consuming task. This is where U-Net, an architecture for semantic segmentation, has garnered immense popularity.
Wha
Overview of UCTransNet
UCTransNet is an advanced deep learning network used for semantic segmentation tasks. The network is based on U-Net architecture with modifications to make it more accurate and efficient. The aim of UCTransNet is to eliminate ambiguity and improve segmentation performance by fusing multi-scale channel-wise information.
What is Semantic Segmentation?
Semantic segmentation is a computer vision task that involves assigning labels or categories to each pixel in an image. T
UNet++ is an innovative architecture for semantic segmentation that builds on the foundations of the U-Net. Semantic segmentation is the operation of assigning each pixel of an image a label, like whether it represents a human, a dog or a tree. This operation is of great importance in the field of medical image segmentation where microscopic details need to be examined carefully.
The Difference between UNet and UNet++
The U-Net is a neural network architecture that has been widely used to gen
What is YOLOP?
YOLOP is a new technology in the field of self-driving cars that stands for "You Only Look Once Perception". It is a driving perception network that performs multiple tasks simultaneously such as traffic object detection, drivable area segmentation, and lane detection. YOLOP uses a lightweight CNN to extract image features which are then fed to three decoders to complete their respective tasks. YOLOP is considered as a lightweight version of Tesla's HydraNet self-driving vehicle