Bilateral grid is a powerful data structure that is used to process images in real-time. This innovative technology is specifically designed to perform edge-aware image manipulation, such as local tone mapping, on high-resolution images.
What is Bilateral Grid?
Bilateral grid is a data structure used in computer graphics and image processing applications. Unlike other image processing techniques, which operate on individual pixels, bilateral grid processes entire neighborhoods of pixels at on
When we talk about composite fields, we are referring to a concept in computer science where a single data field is created by combining multiple primitive fields. It is a technique that is commonly used in databases and programming languages, and it allows for more efficient and organized data management.
What are Primitive Fields?
Primitive fields are individual data fields that contain a single, simple value. Examples of primitive fields include integers, strings, and booleans. These field
What is CLIP?
Contrastive Language-Image Pre-training (CLIP) is a method of image representation learning that uses natural language supervision. It involves training an image encoder and a text encoder to predict the correct pairings of a batch of (image, text) training examples. During testing, the learned text encoder synthesizes a zero-shot linear classifier by embedding the names or descriptions of the target dataset’s classes.
How Does CLIP Work?
CLIP is pre-trained to predict which of
The Laplacian Pyramid: A Linear Invertible Image Representation
The Laplacian Pyramid is a linear invertible image representation consisting of a set of band-pass images spaced an octave apart, plus a low-frequency residual. In other words, it captures the image structure present at a particular scale, making it useful for various image processing tasks such as compression, image enhancement, and texture analysis.
To understand how the Laplacian Pyramid works, we need to first understand the G
Introduction to ScatNet
ScatNet is a wavelet scattering transform that uses a deep convolution network architecture. It's useful in computing a translation-invariant representation, which is stable to deformations. This transform computes non-linear invariants by utilizing modulus and averaging pooling functions. It helps to eliminate the variability of an image due to translation and deformations.
Wavelet Scattering Transform
The wavelet scattering transform is a method of transforming an i
VirText, which stands for Visual representations from Textual annotations, is a method of learning visual representations through semantically dense captions. This approach uses a combination of ConvNet and Transformer learning to generate natural language captions for images. Once these captions have been generated, the learned features can then be transferred to downstream visual recognition tasks.
How Does VirText Work?
VirText is a pre-training approach that uses natural language captions