BiSeNet V2: Overview of a Real-Time Semantic Segmentation Architecture
What is BiSeNet V2?
If you haven’t heard of BiSeNet V2, you’re not alone. However, if you’re interested in real-time semantic segmentation, this two-pathway architecture could be exactly what you’ve been looking for. BiSeNet V2 is designed to capture spatial details with a wide channel, shallow layer pathway called Detail Branch, as well as to extract categorical semantics with a narrow channel, deep layer pathway called S
BASNet, or Boundary-Aware Segmentation Network, is an innovative technology used for highly accurate image segmentation. This architecture is composed of a predict-refine architecture and a hybrid loss.
The Predict-Refine Architecture
The predict-refine architecture is the first component of BASNet. Composed of a densely supervised encoder-decoder network and a residual refinement module, this component is designed to predict and refine a segmentation probability map.
Hybrid Loss
The hybri
Overview of CascadePSP: A General Segmentation Refinement Model
CascadePSP is an advanced model used to refine segmented images from low to high resolution. This model takes an initial mask as input and generates a refined mask as the output. It is designed to work in a cascade fashion, which means it generates refined segmentation in a coarse-to-fine manner. Coarse outputs from the early levels predict object structure which will be used as the input to the latter levels to refine boundary det
CABiNet: A Context Aggregation Network for Efficient Semantic Segmentation
As the demand for autonomous systems continues to increase, there is a greater need for efficient, real-time visual scene understanding. To address this need, researchers have proposed the Context Aggregation Network (CABiNet), a dual-branch convolutional neural network designed for pixelwise semantic segmentation.
Compared to other state-of-the-art methods, CABiNet offers significantly lower computational costs without
Criss-Cross Network (CCNet) is an image processing technology that aims to gather contextual information for every pixel in an image. The technology uses a criss-cross attention module that harvests contextual information and a recurrent operation to capture full-image dependencies. This technology has several advantages over other similar technologies.
Why is CCNet important?
Image recognition and processing are critical tasks in the current digital era. With the rise of artificial intellige
DeepLab is a powerful semantic segmentation tool used to identify objects within digital images. The process begins by using dilated convolutions to analyze the input image. Then, the resulting output is bilinearly interpolated and processed through a fully connected CRF, which fine-tunes the prediction accuracy to generate the final result.
What is Semantic Segmentation?
Semantic segmentation is a process of identifying specific objects within an image and separating them from their backgrou
DeepLabv2: An Overview of Semantic Segmentation Architecture
What is Semantic Segmentation?
In image processing, semantic segmentation is the process of labeling each pixel in an image according to its semantic meaning, such as object or background. This technique is commonly used in computer vision applications like autonomous driving, medical imaging, and satellite imagery analysis. Semantic segmentation has many important applications in the field of artificial intelligence, and DeepLabv2
What is DeepLabv3?
DeepLabv3 is a new and improved semantic segmentation architecture that builds on the success of its predecessor, DeepLabv2. Semantic segmentation is the process of separating an image into multiple segments or regions, each of which represents a different object or part of an object. DeepLabv3 uses several modules, including atrous convolution and Atrous Spatial Pyramid Pooling, to capture multi-scale context and improve the accuracy of object recognition and labeling.
How
Interactive segmentation is a popular technique used in computer vision that enables humans to interactively add or remove regions of an image based on their understanding of the scene. One recent technique that has garnered attention in this area is EdgeFlow, which fully utilizes interactive information of user clicks with edge-guided flow.
What is Edge Guidance?
Edge guidance is the idea that interactive segmentation improves segmentation masks progressively with user clicks. As users click
EfficientDet: Revolutionizing Object Detection
Object detection is a critical task in computer vision that involves locating and classifying objects within an image. It has a wide range of applications, from self-driving cars to surveillance systems to medical imaging. One of the most powerful and efficient object detection models is EfficientDet, which has recently gained popularity due to its outstanding performance and speed.
Optimizing Object Detection
EfficientDet is an object detection
The EfficientUNet++ is an advanced neural network architecture designed for efficient and accurate image segmentation tasks. It combines the decoder architecture inspired on the UNet++ structure with the EfficientNet building blocks to achieve higher performance and lower computational complexity.
UNet++ and EfficientNet building blocks
The UNet++ structure is a popular encoder-decoder architecture used for semantic segmentation tasks. It consists of a series of convolutional and pooling laye
What is ENet?
ENet is a type of neural network used for semantic segmentation, which is the process of dividing an image into different segments to identify objects or areas within the image. The architecture of ENet is designed to be compact and efficient, while still producing accurate results.
How Does ENet Work?
The ENet architecture uses a combination of several techniques to achieve its goals. One important design choice is the use of the SegNet approach to downsampling, which involves
What is ESPNet?
ESPNet is a special type of neural network that helps analyze and understand high-resolution images. It does this by "segmenting" the image, or dividing it into smaller parts that can be analyzed more easily. This segmentation helps the network better understand what is in the image and make more accurate predictions.
How does ESPNet work?
ESPNet uses something called a "convolutional module," which is a type of algorithm that helps process and analyze images. Specifically, i
Are you interested in understanding how machines can perceive the world around them? Well, Fully Convolutional Networks (FCNs) might be the answer to your questions. FCNs are an architecture used mainly for semantic segmentation. They have proven to be quite effective in image recognition and other machine learning applications which require machines to understand their surroundings and make decisions based on that.
The Anatomy of Fully Convolutional Networks (FCNs)
FCNs use solely locally co
In the field of computer vision, a new concept called "dense connections" has become very popular. Dense connections help improve the flow of information during the training of neural networks, which can lead to better results in tasks like image classification. This concept has been applied in a network called DenseNet, which has shown impressive performances in natural image classification tasks. However, now researchers have proposed a new network called HyperDenseNet that takes this concept
K-Net: A Unified Framework for Semantic and Instance Segmentation
K-Net is a framework for semantic and instance segmentation that uses a set of learnable kernels to consistently segment instances and semantic categories in an image. This framework uses a simple combination of semantic kernels and instance kernels to allow panoptic segmentation. It learns the kernels by using a content-aware mechanism that ensures each kernel responds accurately to varying objects.
How K-Net Works
K-Net uses
What is LiteSeg?
LiteSeg is a new method for creating faster, more efficient models for semantic segmentation. It uses several advanced techniques, including a deeper version of the Atrous Spatial Pyramid Pooling module and depthwise separable convolution.
Background on Semantic Segmentation
Semantic segmentation is a computer vision technique that involves labeling every pixel in an image with a specific category. For example, in a scene with a dog and a cat, semantic segmentation would lab
Introduction:
nnFormer, or not-another transFormer, is a computer model used for semantic segmentation. Semantic segmentation is a technique used to label each pixel in an image with a particular object or scene it belongs to. For example, in an image of a street, each car, pedestrian, and building would be labeled separately using semantic segmentation. nnFormer is designed to help computers better understand images, allowing for more accurate vision-based applications.
Architecture:
The nn