Mobile Video Network, or MoViNet, is a novel technology that allows for efficient video network computation and memory. It is designed to work on streaming videos for online inference. The technique includes three main elements that optimize efficiency while lowering the peak memory usage of 3D Convolutional Neural Networks (CNNs).
Neural Architecture Search
The first step in developing MoViNet involved creating a video network search space and employing neural architecture search. The goal w
What is MPNet and How Does it Work?
MPNet is a pre-training method for language models that combines two approaches, masked language modeling (MLM) and permuted language modeling (PLM), to create a more efficient and effective model. It was designed to address the issues of two other popular pre-training models, BERT and XLNet. MPNet takes into consideration the dependency among predicted tokens and alleviates the position discrepancy of XLNet by utilizing the position information of all tokens
Overview of MPRNet
MPRNet is a cutting-edge technology that aids image processing experts in restoring degraded input. It is a multi-stage progressive image restoration architecture, which means that it breaks down the overall recovery process into manageable steps. As a result, restoration becomes quicker and more efficient.
Image restoration is the process of improving the quality of a digital image that has been degraded by noise, blur, or other unwanted artifacts. MPRNet learns the restora
MT-PET: A Multi-Task Approach to Exaggeration Detection
If you're interested in natural language processing, you might have heard of PET, or Pattern Exploiting Training. It's a technique that uses masked language modeling to transform tasks into cloze-style question answering tasks, making them easier to solve. It has been shown to be effective in few-shot learning, where there is only a small amount of data available for training. However, a new technique called MT-PET takes this idea to the n
MT5: Multilingual Natural Language Processing Advancement
What is MT5?
MT5 is a natural language processing (NLP) advancement that is designed to handle multiple languages. It is a multilingual variant of T5 that has been pre-trained on a large dataset of over 101 languages. MT5 is used for machine translation, text classification, summarization, and question answering.
Why is MT5 Important?
MT5 is important because it bridges the gap between cross-lingual NLP models and multilingual model
Multi-animal tracking with identification is a field of study that focuses on tracking multiple animals in a given video with the ability to recognize each individual animal's unique features. This field finds its application primarily in wildlife observation and ecological research. Traditionally, biologists and ecologists have been manually tracking animals, which is inefficient and time-consuming. In today's digital age, computer algorithms and artificial intelligence (AI) have made it possib
Overview of Multi-Band MelGAN
Multi-band MelGAN, also known as MB-MelGAN, is an advanced waveform generation model that focuses on high-quality text-to-speech generation. MB-MelGAN improves upon the original MelGAN model by increasing the generator's receptive field and using a multi-resolution STFT loss instead of the feature matching loss to measure the difference between fake and real speech. Additionally, MB-MelGAN is extended with multi-band processing, allowing the generator to take mel-s
Multi-class One-shot Image Synthesis: Generating Images from Few Input Images
Multi-class one-shot image synthesis is an exciting field of research that focuses on generating realistic images from as few as one or more input images. The goal of this approach is to learn a generative model that can produce samples with visual attributes of at least two related classes. This technology has a wide range of applications, including product design, fashion, film, and game development, medical imaging
Multi-DConv-Head Attention (MDHA) is a type of Multi-Head Attention used in the Primer Transformer architecture. It makes use of depthwise convolutions after the multi-head projections. The aim of MDHA is to enable the model to identify and focus on important parts of the input sequence. It achieves this by performing 3x1 depthwise convolutions on the spatial dimension of each dense projection's output. MDHA is similar to Convolutional Attention, which uses separable convolutions instead of dept
Multi-Document Summarization: A Quick Guide
Have you ever struggled to find the most important information from a set of documents or articles? Multi-document summarization is a process that helps to solve this problem. Its goal is to capture the relevant information and provide a short summary by filtering out redundant information.
Approaches to Multi-Document Summarization
There are two primary approaches to multi-document summarization: extractive and abstractive.
Extractive Summarizat
Multi-Frame Super-Resolution: An Introduction to Upscaling Low-Res Images
In the digital era, it's common to take multiple images of the same scene from slightly different angles or at different times. What if you could combine these images into one high-resolution picture with intricate details that none of the original images could provide on their own? That's where Multi-Frame Super-Resolution comes in. In this article, we explore the concept of Multi-Frame Super-Resolution, its applications
Multi-Head Attention is a module for attention mechanisms that allows for the parallel processing of sequence analysis. It is commonly used in natural language processing and neural machine translation systems.
What is Attention?
Attention is a mechanism that allows deep learning models to focus on specific parts of the input sequence when processing information. This can be useful in natural language processing tasks where understanding the meaning of a sentence requires considering the rela
What is Multi-Head Linear Attention?
Multi-Head Linear Attention is a type of self-attention module that is used in machine learning. It was introduced with the help of the Linformer architecture. The idea is to use two linear projection matrices when computing key and value. Multi-Head Linear Attention can help improve the accuracy of computer-based models and reduce the amount of training data that is needed.
How does it work?
Multi-Head Linear Attention works by using two linear projectio
Understanding MHMA: The Multi-Head of Mixed Attention
The multi-head of mixed attention (MHMA) is a powerful algorithm that combines both self- and cross-attentions to encourage high-level learning of interactions between entities captured in various attention features. In simpler terms, it is a machine learning model that helps computers understand the relationships between different features of different domains. This is especially useful in tasks involving relationship modeling, such as huma
A Comprehensive Overview of Multi Loss Functions (BCE Loss + Focal Loss + Dice Loss)
When it comes to image segmentation tasks, choosing the right loss function plays a pivotal role in the overall performance of machine learning models. In recent years, the Combination of multi loss functions has been proven to be a successful approach to improve the results of image segmentation tasks. This article will give an overview of the Multi Loss (BCE Loss + Focal Loss + Dice Loss) function and how it
Multi-modal Dialogue Generation: A Brief Overview
Multi-modal dialogue generation is a rapidly growing field of research that is focused on developing computer systems capable of conversing with humans using multiple modes of communication. Traditionally, dialogue systems have been developed to process text-based interactions. However, with the advent of new technologies such as speech recognition, natural language processing, and computer vision, there is a growing interest in developing syste
Introduction to Multi-Object Tracking
Multi-Object Tracking is a complex task in computer vision that involves detecting and tracking multiple objects in a video sequence. The main goal of this task is to identify and locate objects of interest in each frame of a video and then associate them across frames in order to keep track of their movements over time. This can be achieved by using various algorithms that combine object detection, data association techniques, and motion analysis to accura
MEI is a novel approach that addresses the efficiency--expressiveness trade-off issue in knowledge graph embedding, which has been a challenging task in machine learning. This technique uses the *multi-partition embedding interaction* with block term tensor format to separate the embedding vectors into multiple partitions and learn the local interaction patterns from the data. This way, MEI is able to achieve the optimal balance between efficiency and expressiveness, rather than being exclusivel