What is AttLWB?
AttLWB stands for Attentional Liquid Warping Block. It is a module designed for human image synthesis GANs, which aims to synthesize images of people that look real. AttLWB module propagates source information such as texture, style, color and face identity in both image and feature spaces to the synthesized reference. This process helps the synthesized image to look more natural and similar to the source image.
How AttLWB works?
AttLWB module, firstly, identifies similaritie
Axial Attention is a type of self-attention that is used in high-dimensional data tensors such as those found in image segmentation and protein sequence interpretation. It builds upon the concept of criss-cross attention, which harvests contextual information from all pixels on its criss-cross path in order to capture full-image dependencies. Axial Attention extends this idea to process multi-dimensional data in a way that aligns with the tensors' dimensions.
History and Development
The idea
One of the latest and most innovative additions to image recognition technology is the Big-Little Module, an architecture aimed at improving the performance of deep learning networks. The Big-Little module is a type of block that consists of two branches: the Big-Branch and Little-Branch. This article will provide an overview of this architecture and its applications in image recognition technology.
What are Big-Little Modules?
Big-Little Modules are a type of convolutional neural network (CN
Understanding Bottleneck Residual Blocks in Deep Learning
If you are interested in deep learning and its applications, you must have come across the term "Bottleneck Residual Block" or "Bottle ResBlock." It is a type of residual block commonly used in deep neural network architectures, particularly in ResNets, to reduce the number of parameters and matrix multiplications, while making the model deep and accurate.
What is a Residual Block?
Before we dive into the concept of Bottleneck Residua
What is a Bottleneck Transformer Block?
A Bottleneck Transformer Block is a type of block used in computer vision neural networks to improve image recognition performance. It is a modified version of the Residual Block, which is a popular building block for convolutional neural networks. In this type of block, the traditional 3x3 convolution layer is replaced with a Multi-Head Self-Attention (MHSA) layer. This change allows the network to better understand the relationships between different pa
A Channel Attention Module is a crucial component in convolutional neural networks that helps in channel-based attention. It focuses on 'what' is essential for an input image by using inter-channel relationship of features. In simple terms, it helps in identifying which features in an image are most important and should be focused on.
How does it work?
The Channel Attention Module computes a channel attention map by first squeezing the spatial dimension of the input feature map. This is done
When it comes to machine learning and image processing, the Compact Global Descriptor (CGD) is an important model block for modeling interactions between different dimensions, such as channels and frames. Essentially, a CGD helps subsequent convolutions access useful global features, acting as a form of attention for these features.
What is a Compact Global Descriptor?
To understand what a Compact Global Descriptor is, it may be helpful to first define what is meant by a "descriptor" in this
The Content-Conditioned Style Encoder, also known as COCO, is a type of encoder used for image-to-image translation in the COCO-FUNIT architecture.
What is COCO?
COCO is a style encoder that differs from the traditional style encoder used in FUNIT. COCO takes both content and style images as input, allowing for a direct feedback path during learning. This feedback path enables the content image to influence how the style code is computed, which in turn reduces the direct influence of the styl
What is Contextual Residual Aggregation?
Contextual Residual Aggregation, or CRA, is a state-of-the-art module used for image inpainting. The main function of the module is to fill in missing or damaged parts of an image with realistic and believable content. CRA produces high-frequency residuals for missing contents by weighted aggregating residuals from contextual patches, thus only requiring a low-resolution prediction from the network. Specifically, it involves a neural network to predict a
Convolutional Block Attention Module (CBAM) is an attention module for convolutional neural networks that helps the model better refine its features by applying attention maps along both the channel and spatial dimensions.
What is an Attention Module?
Before diving into CBAM specifically, it's important to understand what an attention module is in the context of neural networks. An attention module is a tool used to help the network focus on important features and ignore irrelevant or noisy d
Overview of CornerNet-Squeeze Hourglass Module
CornerNet-Squeeze Hourglass Module is an image model block used in CornerNet-Lite. It is based on an hourglass module but uses modified fire modules instead of residual blocks. The CornerNet-Squeeze Hourglass Module is used for object detection in images and videos.
What is an Image Model Block?
An image model block is a part of an image processing software that is designed for specific tasks, such as object detection, image recognition or segme
Deep learning models have become immensely popular for a variety of applications such as image classification, speech recognition, and natural language processing. Researchers are constantly striving to develop more efficient and accurate deep learning models to solve these problems. One such model is the CSPResNeXt Block, which was developed to enhance the ResNext Block.
The ResNext Block
The ResNext Block is a type of neural network architecture used in deep learning. This block is a combin
A Dense Block is a module found in convolutional neural networks that directly connects all of its layers (with matching feature-map sizes) with each other. This type of architecture was originally proposed as part of the DenseNet design, which was developed as a solution to the vanishing gradient problem in deep neural networks. By preserving the feed-forward nature of the network, each layer gets additional inputs from all preceding layers and passes on its own feature-maps to all subsequent l
When it comes to object detection in computer vision, the Depthwise Fire Module is a new technique that is gaining attention. This module is a variant of the original Fire Module, which has been used for its effectiveness in deep learning models. The Depthwise Fire Module is particularly significant for its improvement in inference time performance, which is an essential factor in real-time applications such as autonomous driving, robotics, and surveillance.
Fire Module
The Fire Module is a w
DiCE Units are image model blocks that utilize dimension-wise convolutions and dimension-wise fusion to efficiently encode spatial and channel-wise information contained in an input tensor. These convolutional filtering techniques apply lightweight operations across each dimension of the input tensor, allowing for efficient encoding without the computationally intensive requirements of standard convolutions.
Improving Convolutional Efficiency
Standard convolutions function through the simulta
Dilated Bottleneck Block is a type of image model block used in the DetNet convolutional neural network architecture. This block structure utilizes dilated convolutions to enlarge the receptive field effectively, making it an efficient way to analyze images.
What is Dilated Convolution?
Convolution is a mathematical operation applied to images to extract information using a set of predefined filters, also known as kernels. A convolutional neural network employs convolution layers to produce f
Dilated Bottleneck with Projection Block: An Overview of an Image Model Block
Convolutional neural networks (CNNs) have revolutionized the field of computer vision by improving image recognition systems’ accuracy. However, deeper CNNs have high computational costs and tend to suffer from vanishing gradients, making them less effective. To solve this problem, researchers have developed the Dilated Bottleneck with Projection Block.
What is the Dilated Bottleneck with Projection Block?
The Dila
DimFuse: A New Image Model Block for Efficient Feature Combination
Convolution is a popular technique in image processing, where it involves combining different features to produce a final output. However, point-wise convolution can be computationally expensive, especially when dealing with large images. That's where Dimension-wise Fusion, or DimFuse, comes in. It is an efficient model block that can combine features globally without requiring too many computations.
The Limitations of Point-W