A BiFPN, also known as a Weighted Bi-directional Feature Pyramid Network, is a type of feature pyramid network that helps with easy and fast multi-scale feature fusion. The network incorporates multi-level feature fusion techniques from FPN, PANet, and NAS-FPN, which allow information to flow both top-down and bottom-up while using regular and efficient connections. The BiFPN is designed to treat input features with varying resolutions equally, which is different from traditional approaches that
Bottom-Up Path Augmentation is a technique that enhances feature pyramids with accurate localization signals found in low-levels. By shortening the information path, it can improve the accuracy of identifying object instances in images.
How Does Bottom-Up Path Augmentation Work?
Bottom-Up Path Augmentation involves building blocks that take a higher resolution feature map and a coarser map and generate a new feature map. Each feature map goes through a 3x3 convolutional layer with a stride of
Context Enhancement Module for Object Detection
In object detection, the Context Enhancement Module (CEM) is a feature extraction module used specifically in ThunderNet which enlarges the receptive field. The aim of the CEM is to aggregate multi-scale local context information and global context information to generate more discriminative features.
The Key Concepts of CEM
CEM is designed to merge feature maps from three scales - C4, C5, and Cglb. Cglb is the global context feature vector obt
DLA: Improving Neural Network Accuracy and Efficiency
Deep Layer Aggregation (DLA) is a technique used to improve the accuracy and efficiency of neural networks. DLA accomplishes this by iteratively and hierarchically merging the feature hierarchy across layers in a neural network to create networks with fewer parameters and higher accuracy.
In the process of DLA, there are two different approaches: Iterative Deep Aggregation (IDA) and Hierarchical Deep Aggregation (HDA). In IDA, the feature a
Overview of FFMv1: A Feature Fusion Module from the M2Det Object Detection Model
FFMv1, or Feature Fusion Module v1, is a component of the M2Det object detection model. Feature fusion modules play an essential role in creating the multi-level feature pyramid required for object detection. They utilize 1x1 convolution layers to reduce the channels of input features and a concatenation operation to combine feature maps. FFMv1 involves two feature maps from different scales in the backbone and a s
Feature Fusion Module v2, or FFMv2, is an important module in the object detection model known as M2Det. Its primary function is to combine the features from different levels to create a final, multi-level feature pyramid.
What is M2Det?
M2Det is an object detection model that aims to accurately and efficiently detect objects within an image. The model is based on the concept of feature pyramids, which involves combining features at multiple scales to achieve better accuracy.
What is a feat
What is Feature Intertwiner?
Feature Intertwiner is a revolutionary module used for object detection that focuses on leveraging the features of a more reliable set of data to guide the feature learning of a less reliable set. With Feature Intertwiner, there is a mutual learning process that enables two sets to have a closer distance within the cluster in each class.
How Does Feature Intertwiner Work?
Feature Intertwiner is specifically developed to be used on the object detection task. To ad
What is a Feature Pyramid Network?
A **Feature Pyramid Network**, or **FPN**, is an artificial neural network used for object detection in images. Specifically, it is a feature extractor that takes a single-scale image of an arbitrary size as input and outputs proportionally sized feature maps at multiple levels. This allows for the detection of objects at different scales within an image.
How Does FPN Work?
The construction of the pyramid involves a bottom-up pathway and a top-down pathway.
Are you interested in learning about cutting-edge technology in the field of object detection? Look no further than FSAF, or Feature Selective Anchor-Free. This innovative building block can revolutionize single-shot object detectors, improving upon the limitations of conventional anchor-based detection.
What is FSAF?
FSAF is a feature selection anchor-free module that can be added to single-shot detectors with a feature pyramid structure. It addresses two major limitations associated with co
Overview of MatrixNet
MatrixNet is a new technology that helps computers detect objects of different sizes and aspect ratios. It is used in computer vision, which is a field of computer science that helps computers "see" and understand the world around us.
MatrixNet uses several matrix layers, each of which handles an object of a specific size and aspect ratio. These layers can be thought of as building blocks that work together to detect objects in images or videos.
MatrixNet is an alternati
Overview of McKernel: A Framework for Kernel Approximates in the Mini-Batch Setting
McKernel is a framework introduced to use kernel approximates in the mini-batch setting with Stochastic Gradient Descent (SGD) as an alternative to Deep Learning. This core library was developed in 2014 as an integral part of a thesis at Carnegie Mellon and City University of Hong Kong. The original intention was to implement a speedup of Random Kitchen Sinks by writing a very efficient HADAMARD transform, which
What Is Multi-Level Feature Pyramid Network (MLFPN)?
Multi-Level Feature Pyramid Network, or MLFPN for short, is a type of feature pyramid block used in object detection models. Specifically, it is used in the popular M2Det model. The purpose of MLFPN is to extract representative, multi-level, and multi-scale features to aid in object detection.
How Does MLFPN Work?
The MLFPN works by fusing multi-level features extracted by a backbone as a base feature. It then feeds this into a block of al
If you've ever used an image recognition tool or a video encoder, you've likely utilized convolutional neural networks (CNNs). CNNs allow for automated, accurate image and video recognition, and they've revolutionized the way we use visual media. However, not all CNNs are created equal - some architectures are more efficient and accurate than others. That's where NAS-FPN comes in.
What is NAS-FPN?
NAS-FPN (Neural Architecture Search Feature Pyramid Network) is a CNN architecture that was disc
Overview of NEAT, Neural Attention Fields
NEAT, or Neural Attention Fields, is a feature representation for end-to-end imitation learning models. It is a technique used to compress high-dimensional 2D image features into a compact representation by selectively attending to relevant regions in the input while ignoring irrelevant information. This way, the model associates the images with the Bird's Eye View (BEV) representation, which facilitates the driving task. In this article, we will explor
Understanding PAFPN in Path Aggregation Networks (PANet)
Have you ever heard of PAFPN? It's a feature pyramid module that's used in Path Aggregation networks (PANet). This module helps combine FPNs with bottom-up path augmentation. But what does all of this really mean?
Well, let's start by understanding what PANet is. You see, PANet is a neural network architecture that's used for object detection in images. It's used in many different applications such as autonomous vehicles and security cam
A **Panoptic FPN** is a computer vision technique that is used to perform both instance segmentation and semantic segmentation of an image. It is an extension of the popular FPN algorithm, which uses a feature pyramid to detect and segment objects in an image. The Panoptic FPN adds a new branch for performing semantic segmentation, which allows it to recognize both objects and the background in an image.
What is FPN?
FPN (Feature Pyramid Network) is a popular computer vision technique that is
Understanding Receptive Field Block (RFB)
If you are someone who is interested in computer vision and image detection, you may have come across the term Receptive Field Block or RFB. Receptive Field Block is a module that enhances the deep features learned from lightweight Convolutional Neural Network (CNN) models for fast and accurate image detection, especially in object recognition tasks. In this article, we will dive deeper into the concept of RFB and learn how it works to improve the accur
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 SF