In human action recognition, each type of action generally only depends on a few specific kinematic joints. Furthermore, over time, multiple actions may be performed. To address these observations, Song et al. proposed a joint spatial and temporal attention network based on LSTM, called STA-LSTM, to adaptively find discriminative features and keyframes. This network combines a spatial attention sub-network and a temporal attention sub-network to select important regions and key frames.
What is
Overview of Spatio-Temporal Features Extraction
If you're interested in understanding how things move, then you've likely come across the term "spatio-temporal" before. This refers to anything that has both a spatial (where) and a temporal (when) component to it. By analyzing these components, we can extract features that tell us a lot about how things move and change over time.
One important use of spatio-temporal features extraction is in the field of stability measurement. Essentially, this
Speaker diarization is a process that involves separating and labeling audio recordings by different speakers. The main goal is to identify and group together segments of speech that belong to the same person, which allows for the transcription of spoken words to be more accurate and detailed. This process is most commonly used in the field of speech recognition, where it is critical to be able to understand who is speaking during an audio recording.
How Does Speaker Diarization Work?
The pro
Speaker recognition, also known as voice recognition, is a process that involves identifying or confirming the identity of a person based on their speech. This technique is used in various fields, including security, law enforcement, and telecommunication, for authentication purposes.
How Speaker Recognition Works
The process of speaker recognition involves analyzing speech signals to extract features that are specific to each individual's voice. These features are used to create a unique voi
Speaker-Specific Lip to Speech Synthesis is an area of scientific study that is attempting to accurately understand and interpret a person’s speech style and content through the analysis of their lip movements. This concept has gained interest in recent years because of its potential to enhance human-to-machine communication, particularly in scenarios where the speaker’s voice cannot be heard, such as in noisy public areas or in underwater communication channels.
What is Lip to Speech Synthesi
Speaker verification is the process of confirming the identity of a person through the characteristics of their voice. This technology is used in various industries, including banking, security, and law enforcement.
How Does Speaker Verification Work?
Speaker verification works by analyzing unique features of an individual’s voice, such as their pitch, cadence, and pronunciation. The process involves recording a person speaking and extracting specific features that can identify them. These fe
SpecGAN is a computational model designed to produce sound samples that mimic human-made sounds. This process is called generative audio, and it utilizes artificial intelligence to create complex sound samples. SpecGAN is made using generative adversarial network methods, which is a type of artificial neural network.
The Problem with Generating Audio Using GAN
GANs are a popular method used for image generation, but they aren't suitable for producing audio because of how complex sound waves a
Spectral clustering is a method used for clustering data points together based on their similarities. It is becoming increasingly popular in the field of machine learning because it is very effective at dealing with datasets that are not easily separable.
What is Spectral Clustering?
Spectral clustering is a method used for clustering data points together based on their similarities. It is based on the eigenvalues and eigenvectors of a matrix called the graph Laplacian, which is used to repre
What is Spectral Dropout?
Spectral Dropout is a method used in machine learning to improve the performance of deep learning networks. It is a regularization technique that helps to prevent neural networks from overfitting to the training data, improving their ability to generalize to new and unseen data.
At its core, Spectral Dropout is a modification of the traditional dropout method commonly used in deep learning networks. Dropout is a technique that involves randomly dropping out some of th
GAP-Layer is a graph neural network layer that helps to optimize the spectral gap of a graph by minimizing or maximizing the bottleneck size. The goal of GAP-Layer is to create more connected or separated communities depending on the mining task required.
The Spectral Gap Rewiring
The first step in implementing GAP-Layer is to minimize the spectral gap by minimizing the loss function. The loss function is given by:
$$ L\_{Fiedler} = \|\tilde{\mathbf{A}}-\mathbf{A}\| \_F + \alpha(\lambda\_2)^
Spectral Normalization is a technique used for Generative Adversarial Networks (GANs). Its purpose is to stabilize the training of the discriminator. It does this by controlling the Lipschitz constant of the discriminator through the spectral norm of each layer. Spectral normalization has the advantage that the only hyper-parameter that is needed to be tuned is the Lipschitz constant.
What is Lipschitz Norm?
Lipschitz norm of a function is a property that is used in mathematical analysis to d
Spectral-Normalized Identity Priors, also known as SNIP, is a pruning technique that helps improve the efficiency of artificial intelligence models. This method penalizes an entire residual module in a Transformer model towards an identity mapping, which means the model adjusts the function to keep it as close to the original as possible. SNIP can be applied to structured modules like an attention head, an entire attention block, or a feed-forward subnetwork.
What is SNIP?
Spectral-Normalized
Overview of SNGAN:
SNGAN, or Spectrally Normalised GAN, is a powerful type of generative adversarial network that can be used to generate images, videos, and other types of media. It is a type of neural network that is composed of two parts: a generator and a discriminator.
The generator works to create and output new data that is based on the patterns and features that it has learned from the training data. The discriminator, on the other hand, works as a classifier to determine whether the g
Speech recognition is an advanced technology used to convert human speech into written text. This process is also known as automatic speech recognition (ASR) and uses different algorithms to detect and analyze human speech, providing a written transcript of a recording or live speech.
How Speech Recognition Works
Speech recognition technology is based on a combination of computer science, linguistics, and pattern recognition. It uses machine learning and artificial intelligence to analyze and
Speech Separation: An Introduction
Speech Separation is a process of extracting overlapping speech sources from a mixed speech signal. This special scenario of the source separation problem is based on the study of the overlapping speech signal sources. This process filters out other interferences like music or noise signals that are not relevant to the study.
What is Speech Separation?
As the name suggests, Speech Separation is a process of dividing speech signals into individual sources. T
Speed is a critical factor in many computer vision tasks, such as scene understanding and visual odometry, which are essential components in autonomous and robotic systems. The ability to estimate depth from a single frame is called monocular depth estimation (MDE), and it is an essential skill for many computer vision applications. However, vision transformer architectures are too deep and complex for real-time inference on low-resource platforms. This is where the Separable Pyramidal pooling E
SpineNet: A Scalable Neural Network for Object Detection
If you are familiar with computer vision algorithms, you might have heard of Convolutional Neural Networks (CNNs) before. CNNs are widely used in object detection and recognition tasks. However, the biggest challenge of using these networks is that they require high computational resources, making them difficult to use in real-time applications such as autonomous vehicles, drones or mobile devices.
That's where SpineNet comes in. It is a
Split attention is a technique used in machine learning to improve the performance of neural networks. It allows for attention across feature-map groups, which can be divided into several cardinal groups. This is done by introducing a new hyperparameter called the radix, which determines the number of splits within a cardinal group.
How Split Attention Works
The split attention technique involves applying a series of transformations to each individual group, resulting in an intermediate repre