What is ASFF?
ASFF, which stands for Adaptively Spatial Feature Fusion, is a powerful method for pyramidal feature fusion. Essentially, it helps neural networks learn how to spatially filter and combine features from multiple levels in a pyramid, in order to create more accurate object detection models. ASFF helps to suppress inconsistent or conflicting information by selecting only the most useful features for combination.
How does ASFF work?
ASFF operates by first integrating and resizing
The Balanced Feature Pyramid (BFP) is a feature pyramid module used for object detection. Unlike other approaches like FPNs that integrate multi-level features using lateral connections, the BFP strengthens the features using the same deeply integrated balanced semantic features. This results in improved information flow and better object detection results.
How the BFP Works
The BFP pipeline consists of four steps: rescaling, integrating, refining, and strengthening. The features at resolutio
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
What is the Exact Fusion Model (EFM)?
The Exact Fusion Model, or EFM for short, is a technique used to aggregate a feature pyramid. It is based on a machine learning algorithm called YOLOv3, which assigns one bounding box per ground truth object. The EFM is designed to assemble features from three different scales to better detect objects in an image.
How does the EFM work?
The EFM uses anchor boxes to assign bounding boxes to objects in an image. Each ground truth bounding box is matched wi
Have you ever heard of Feature Pyramid Grids, or FPG? It may sound complicated, but it’s actually a deep learning method that’s used for image analysis and recognition. FPG is a multi-pathway feature pyramid that represents the feature scale-space as a regular grid of parallel bottom-up pathways, which are fused by multi-directional lateral connections.
What is FPG?
FPG is a method that connects the backbone features of a convolutional neural network (ConvNet) with a regular structure of para
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.
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
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
What is an RFP?
An RFP or Recursive Feature Pyramid is a type of network used to enhance object detection. It builds on top of Feature Pyramid Networks (FPN) by adding extra feedback connections from the FPN layers into the backbone layers. This recursive structure boosts performance and speeds up training by bringing features that receive gradients from detector heads back to the low levels of the backbone.
How does an RFP Work?
Unrolling the recursive structure to a sequential implementati