Cascade R-CNN is an advanced object detection architecture that seeks to solve the problem of degrading performance with increased IoU thresholds. This overfitting of training and inference-time mismatch between optimal detector and inputs has become a crucial challenge in machine learning. This article will discuss the structure of the Cascade R-CNN architecture and how it addresses the overfitting problem.
The Cascade R-CNN Model
Cascade R-CNN is a multi-stage extension of the R-CNN model,
CenterNet is an innovative one-stage object detector that uses a triplet detection method instead of the traditional pair. It improves recognition accuracy by utilizing two customized modules, namely, cascade corner pooling and center pooling. These modules collect rich information from the top-left and bottom-right corners and provide more recognizable information at the central regions of an object.
How does CenterNet work?
CenterNet is an efficient object detection framework that can accur
Overview of CentripetalNet
CentripetalNet is a complex computer system that serves as a keypoint-based detector. It uses a special technique called centripetal shift to match corner keypoints from the same object or instance. The system accomplishes this by predicting the position and centripetal shift of the corner points and matching corners whose shifted results are aligned.
Centripetal shift is a technique where an object's keypoints are shifted in a way that focuses them towards the cente
CoVA or Context-Aware Visual Attention-based end-to-end pipeline for Webpage Object Detection is a technology that aims to predict labels for a webpage containing various elements. This prediction is made by learning function f.
What Does CoVA Consist Of?
CoVA receives three inputs: a screenshot of a webpage, a list of bounding boxes, and neighborhood information for each element obtained from the DOM tree.
The technology uses four stages to process this information:
Stage 1: Graph Represe
What is CPN?
CPN, also known as the Contour Proposal Network, is a cutting-edge technology used to detect and identify objects in images. Specifically, CPN is used to identify possibly overlapping objects in an image while simultaneously creating closed object contours that are incredibly precise down to the pixel level. CPN is considered a state of the art technology in the field of object detection and is capable of effectively integrating with other object detection architectures, making a f
What is CornerNet-Saccade?
CornerNet-Saccade is an advanced version of CornerNet, which is an object detection model that can identify the corners of an object in an image. The CornerNet-Saccade model adds an attention mechanism, which operates similar to saccades in human vision, to more efficiently and effectively locate objects within an image.
How does CornerNet-Saccade work?
CornerNet-Saccade uses a multi-stage process to detect objects in an image. First, the full image is reduced in s
CornerNet-Squeeze is a cutting-edge object detector that builds on the innovation of CornerNet. By integrating a new, compact hourglass architecture that utilizes fire modules with depthwise separable convolutions, CornerNet-Squeeze can detect objects in a more streamlined and efficient manner.
What is CornerNet?
Before delving into the specifics of CornerNet-Squeeze, it’s important to understand the foundational technology it builds upon: CornerNet. Developed by the University of California,
CornerNet Overview: Object Detection Made Simple
If you've ever wondered how computers are able to recognize objects in pictures, one of the techniques used is called object detection. This involves a machine learning model that can identify where objects are located in an image by drawing a bounding box around them. One of the latest object detection models available is called CornerNet.
CornerNet takes a unique approach to object detection by detecting an object bounding box as a pair of key
Overview of DAFNe
DAFNe is a deep neural network used for oriented object detection. It is a model that performs predictions on a dense grid over the input image, being architecturally simpler in design as well as easier to optimize compared to its two-stage counterparts. The model reduces prediction complexity by not employing bounding box anchors, which leads to a better separation of bounding boxes especially in the case of dense object distributions.
One of the core features of DAFNe is it
Deformable DETR is a type of object detection method that is helping to solve some of the problems with other similar methods. It combines two important things, sparse spatial sampling and relation modeling, to create a better result.
What is Deformable DETR?
Deformable DETR is a type of object detection method that uses a combination of sparse spatial sampling and relation modeling, which helps to solve some of the problems with other similar methods. It uses a deformable attention module, w
What is Detr?
Detr is a state-of-the-art object detection model that uses a Transformer network with a convolutional backbone to detect objects in images. Object detection is a computer vision task that involves identifying objects and their locations within an image. Detr has achieved state-of-the-art performance on several standard benchmarks and has demonstrated its effectiveness in real-world applications.
How Does Detr Work?
Detr uses a convolutional neural network (CNN) backbone to ext
Introduction to Dynamic R-CNN
Dynamic R-CNN is an object detection technology that improves upon previous two-stage object detectors. The main issue with the previous method was that the fixed network settings and dynamic training procedure led to inconsistencies that made it challenging to train high-quality detectors. Dynamic R-CNN solves this problem by adjusting the label assignment criteria and regression loss function based on the statistics of proposals during training.
Components of D
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
Overview of ExtremeNet
ExtremeNet is an advanced object detection framework that detects the four extreme points (top-most, left-most, bottom-most, right-most) of an object. This framework uses a keypoint estimation approach to locate extreme points by predicting multi-peak heatmaps for each object category. Additionally, ExtremeNet uses one heatmap per category to predict the object center, by calculating the average of two bounding box edges in both the x and y dimensions.
How ExtremeNet Wo
Object detection is an important task in computer vision where the goal is to identify and locate objects within an image. One approach to solving this problem is through the use of two-stage object detectors which first propose regions of interest before classifying and refining these regions. F2DNet is a new two-stage object detection architecture which improves upon classical two-stage detectors.
What is F2DNet?
F2DNet is a novel two-stage object detection architecture which aims to elimin
Fast R-CNN is an object detection model which is an improvement over its predecessor, R-CNN. It aims to identify objects in an image by aggregating CNN features into a single forward pass instead of extracting them independently for each region of interest. This enables regions of interest from the same image to share computation and memory, making the model faster and more efficient than its predecessor.
What is Object Detection?
Object detection is a computer vision task that involves ident
Faster R-CNN: An Improved Object Detection Model
If you’re interested in object detection models, then you might have heard about Faster R-CNN. Faster R-CNN is an object detection model, which is an algorithm that analyzes an image or a video and identifies objects in the scene. Object detection models are incredibly useful for many things, such as self-driving cars, image search engines, face recognition, and more.
Faster R-CNN improves upon previous models, such as Fast R-CNN, by using a reg
Introduction to FCOS: An Anchor-Box Free Object Detection Model
If you're someone who is interested in computer vision, you might have come across the term "object detection". Object detection is a crucial task in computer vision, where the objective is to detect objects present in an image or video. Over the past few years, many object detection models have been developed, and one such model is called FCOS.
FCOS stands for Fully Convolutional One-Stage Object Detection, and it is an anchor-bo