AffCorrs for One-Shot Visual Search of Object Parts
Have you ever noticed how easy it is for humans to recognize objects in a scene, even if the objects are partially obscured or shown from different angles? This is because our brain is able to match parts of the object we see with parts of a mental representation we have built over time. This process is called part correspondence, and it is essential for many computer vision tasks. Researchers have been working on developing algorithms that ca
What is BlendMask?
BlendMask is a type of computer program that helps researchers and engineers better understand images by dividing them into different parts called "instances." This process of separating an image into different pieces is called "instance segmentation." BlendMask is built on top of another program called FCOS, which is used for detecting objects in an image. BlendMask uses features from an image or inputs from other programs before predicting a set of bases, which is used to c
Cascade Mask R-CNN is a powerful computer vision model that extends Cascade R-CNN to instance segmentation. This means that it can identify and segment each individual object in an image, providing precise boundaries around them.
What is Cascade R-CNN?
Cascade R-CNN is a type of object detection model that uses a series of convolutional neural networks (CNNs) to identify and locate objects in an image. It works by dividing the image into smaller patches, and then using a series of CNNs to cla
What is CenterMask?
CenterMask is a type of object detection technology that focuses on instance segmentation. This means that it is capable of detecting individual objects within an image and separating them out into different segments. CenterMask is unique because it is an anchor-free method of instance segmentation, which means that it does not rely on predefined anchors or bounding boxes to detect objects within an image.
How does CenterMask work?
CenterMask works by adding a spatial att
Overview: Understanding CondInst - A New Instance Segmentation Framework
If you're interested in computer vision and object detection, you may have come across the term "instance segmentation". This is a technique used in computer vision to identify and differentiate objects in an image by outlining each object with a unique color code.
CondInst is a new instance segmentation framework that has emerged as an alternative to previous methods. It is a fully convolutional network that can solve in
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
Deep-MAC is a new type of anchor-free instance segmentation model that is based on CenterNet. The objective of this innovation is to deal with the "partially supervised" instance segmentation problem, where all classes have bounding box annotations, but only a subset of classes have mask annotations.
Box Prediction in CenterNet
CenterNet is a model that predicts bounding boxes using three tensors. Firstly, it produces a class-specific heatmap that represents the probability of the center of t
The Global Context Network, or GCNet, is a new technique in image recognition that utilizes global context blocks to model long-range dependencies in images. It builds on the Non-Local Network but reduces the amount of computation required to achieve the same results. GCNet applies global context blocks to multiple layers in a backbone network to construct its models.
What is GCNet?
GCNet is a new technique in computer vision that enables computer programs to recognize objects and patterns in
HTC: The Framework for Cascading in Instance Segmentation
In the field of computer vision, instance segmentation has become an increasingly important task. It involves identifying and classifying objects within an image, while also distinguishing between separate instances of the same object. As this area of research has progressed, different frameworks have been developed in order to perform instance segmentation more efficiently and accurately. One such framework is the Hybrid Task Cascade, o
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
Mask R-CNN: Advancing Object Detection and Instance Segmentation
If you've ever seen a self-driving car, you may wonder how it can understand and track objects on the road. The key lies in object detection and instance segmentation - two critical computer vision techniques that enable machines to identify and classify various objects in an image or video. Among the methods used for these tasks, Mask R-CNN has emerged as a powerful approach that combines the advantages of faster R-CNN and fully
In computer vision, Mask Scoring R-CNN is a state-of-the-art deep learning model used for instance segmentation, which involves identifying objects within an image and labeling each pixel of the object. The model is a variant of the popular Mask R-CNN and improves upon its performance by introducing a MaskIoU Head that predicts the Intersection over Union (IoU) between the predicted mask and the ground truth mask.
What is Mask R-CNN?
To understand Mask Scoring R-CNN, it is necessary to first
Introduction to PANet
Path Aggregation Network, or PANet, is an approach used to enhance information flow in computer vision. Specifically, it seeks to improve instance segmentation frameworks through the use of accurate localization signals in lower layers. In simpler terms, PANet aims to make visual recognition more accurate by reducing the amount of information that gets lost as it travels through neural networks.
What is Instance Segmentation?
Before delving into PANet, it's important to
What is SCNet? An Overview
SCNet, or Sample Consistency Network, is a method for instance segmentation that helps ensure that the results of training are as close as possible to the results at inference time. The goal of SCNet is to make sure that the IoU distribution of the samples, or the intersection over union, is consistent at both training and inference times.
The Importance of Consistent Segmentation
What is instance segmentation? This is the process of identifying different objects w
VisTR: A Transformer-Based Video Instance Segmentation Model
VisTR is an innovative video instance segmentation model based on the popular Transformer architecture. Its approach is designed to simplify and streamline the process of segmenting and tracking instances of objects in a video clip, making it both more efficient and effective.
What is Video Instance Segmentation?
First, let's define what we mean by video instance segmentation. It refers to the process of identifying and tracking in