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VideoBERT

What is VideoBERT? VideoBERT is a machine learning model that is used to learn a joint visual-linguistic representation for video. It is adapted from the powerful BERT model, which was originally developed for natural language processing. VideoBERT is capable of performing a variety of tasks related to video, including action classification and video captioning. How does VideoBERT work? VideoBERT works by encoding both video frames and textual descriptions of those frames into a joint embedd

Viewmaker Network

What is Viewmaker Network? Viewmaker Network is a type of generative model that learns to produce input-dependent views for contrastive learning. This means that it creates different views of an image to help a neural network learn how to distinguish between different images. The network is trained alongside an encoder network and works by creating views that increase the contrastive loss of the encoder network, which helps the neural network learn more effectively. How does Viewmaker Network

ViP-DeepLab

Introduction to ViP-DeepLab ViP-DeepLab is a model used for depth-aware video panoptic segmentation. This model was created by adding a depth prediction head and a next-frame instance branch to the already existing Panoptic-DeepLab model. By doing so, ViP-DeepLab is able to perform video panoptic segmentation and monocular depth estimation simultaneously. What is Depth-Aware Video Panoptic Segmentation? Video panoptic segmentation is a process that includes segmenting objects and backgrounds

VirTex

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

Virtual Batch Normalization

Virtual Batch Normalization is a technique used in the training of generative adversarial networks (GANs) that improves upon the traditional batch normalization method. Batch normalization ensures the outputs of a neural network for a given input sample are dependent on other inputs in the same minibatch, which can affect the network's performance. Virtual Batch Normalization, on the other hand, uses a selected reference batch to normalize inputs and produce more stable outputs than traditional

Virtual Data Augmentation

Virtual Data Augmentation, or VDA, is an advanced technique used in machine learning to improve the quality of language models. It works by fine-tuning pre-trained models using a mixture of virtual data and Gaussian noise. The result is a more robust and accurate language model that is better able to understand and respond to natural language queries. What is Virtual Data Augmentation? Virtual Data Augmentation is a technique used in machine learning to improve the performance and accuracy of

Visformer

Overview of Visformer Visformer is an advanced architecture utilized in the field of computer vision. It is a combination of two popular structures, the Transformer and Convolutional Neural Network (CNN) architectures. This article explains what Visformer is and how it works, discussing the essential features that make it a groundbreaking technology used in computer vision applications. Basic Components of Visformer Visformer architected with Transformer-based features specially designed for

Vision-aided GAN

In recent years, computer scientists have been working on improving the performance of Generative Adversarial Networks (GANs), which are machine learning models capable of generating new data based on a training dataset. One way to improve the performance of GANs is through vision-aided training, which involves using pretrained computer vision models in an ensemble of discriminators. This technique allows the GAN to generate more accurate and diverse outputs, which is particularly useful in appl

Vision-and-Langauge Transformer

Understanding ViLT: A Simplified Vision and Language Pre-Training Transformer Model ViLT is a transformer model that simplifies the processing of visual inputs to match the same convolution-free method used for text inputs. In essence, the model works to improve the interaction between vision and language by pre-training on specific objectives. How ViLT Works ViLT works by pre-training the model using three primary objectives: image-text matching, masked language modeling, and word patch ali

Vision-and-Language BERT

Vision-and-Language BERT, also known as ViLBERT, is an innovative model that combines both natural language and image content to learn task-agnostic joint representations. This model is based on the popular BERT architecture and expands it into a multi-modal two-stream model that processes both visual and textual inputs. What sets ViLBERT apart from other models is its ability to interact through co-attentional transformer layers, making it highly versatile and useful for various applications.

Vision-Language pretrained Model

What is VLMo? VLMo is a technology that helps computers understand both images and text at the same time. This technology is known as a unified vision-language pre-trained model, which means it has been trained to recognize and understand different kinds of data, like pictures and words. Through its modular Transformer network, VLMo has the ability to learn and process massive amounts of visual and textual content. One of VLMo's strengths is its Mixture-of-Modality-Experts (MOME) transformer.

Vision Transformer

Introduction to Vision Transformer The Vision Transformer, also known as ViT, is a model used for image classification that utilizes a Transformer-like architecture over patches of an image. This approach splits the image into fixed-size patches, and each patch is linearly embedded, added with position embeddings, and then fed into a standard Transformer encoder. To perform classification, an extra learnable "classification token" is added to the sequence. What is a Transformer? A Transforme

VisTR

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

Visual Commonsense Reasoning

What is Visual Commonsense Reasoning? Visual Commonsense Reasoning is a growing field within artificial intelligence that aims to teach machines how to understand human-like reasoning in visual contexts. Commonsense knowledge is the understanding that humans have about the world. It is what allows us to make predictions based on certain situations or to infer contextual information. For example, when we see an image of a cat sitting on a table, we can easily predict that the cat might jump off

Visual Commonsense Region-based Convolutional Neural Network

VC R-CNN is a type of computer system that is designed to learn about the objects in pictures in an unsupervised way. This means that the computer can learn from images without being told what to look for. Instead, it uses a method called Region-based Convolutional Neural Network (R-CNN), which is a way of analyzing different regions of an image. It then uses a process called causal intervention to learn about the relationships between different objects in the picture. What is R-CNN? R-CNN is

Visual Commonsense Tests

Visual commonsense tests are designed to gauge a person's ability to understand and interpret visual information. It is a form of intelligence test that focuses on an individual's aptitude for recognizing and making sense of images and other visual stimuli. What are Visual Commonsense Tests? Visual commonsense tests are an important aspect of cognitive psychology. They are used to assess a person's ability to reason about visual information, understand cause and effect, and make inferences fr

Visual Dialog

Introduction to Visual Dialog Visual Dialog is a field of Artificial Intelligence that enables computers to have a meaningful conversation with humans about visual content. In simple terms, it involves answering questions about images through a natural and conversational language with an AI agent. The task involves providing an accurate response to a question, given an image, a dialog history, and a follow-up question about the image. The purpose behind Visual Dialog is to bridge the gap betwee

Visual Entailment

What is Visual Entailment? Visual Entailment (VE) is a task used to predict whether an image and a corresponding written caption match each other and logically cohere. This task usually involves a premise, identified by an image, to be compared against a natural language sentence, instead of another image, as in standard image classification tasks. Aid systems could use this idea to help with improving image captioning and enhancing human-machine interaction. The goal of VE is to identify whet

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Detection Models Mean Shift Clustering Medical Medical Image Models Medical waveform analysis Mesh-Based Simulation Models Meshing Meta-Learning Algorithms Methodology Miscellaneous Miscellaneous Components Mixture-of-Experts Model Compression Model Parallel Methods Momentum Rules Monocular Depth Estimation Models Motion Control Motion Prediction Models Multi-Modal Methods Multi-Object Tracking Models Multi-Scale Training Music Music source separation Music Transcription Natural Language Processing Natural Language Processing Courses Negative Sampling Network Shrinking Neural Architecture Search Neural Networks Neural Networks Courses Neural Search No Code AI No Code AI App Builders No Code Courses No Code Tools Non-Parametric Classification Non-Parametric Regression Normalization Numpy Courses Object Detection Models Object Detection Modules OCR Models Off-Policy TD Control Offline Reinforcement Learning Methods On-Policy TD Control One-Stage Object Detection Models Open-Domain 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