What is Semantic Cross Attention?
Semantic Cross Attention, or SCA, is a technique used in artificial intelligence models to improve the accuracy and efficiency of visual processing. It is based on the cross attention algorithm and involves restricting attention with respect to a semantically-defined mask. The goal of SCA is to either provide feature map information from a semantically restricted set of latents or allow a set of latents to retrieve information in a semantically restricted regio
A semantic reasoning network, or SRN, is a framework designed for scene text recognition that is composed of four components.
Backbone Network
The backbone network is responsible for extracting 2D features from an input image, which are then used to generate 1-D features by the PVAM module.
Parallel Visual Attention Module (PVAM)
The PVAM module generates N aligned 1-D features G, where each feature corresponds to a character in the text and captures the aligned visual information. This me
Semantic Segmentation: An Overview
Have you ever looked at an image and wondered how computers can identify the various objects and their boundaries within an image? That's where semantic segmentation comes into play. Semantic Segmentation is a computer vision task that involves segmenting an image into different classes of objects by assigning each pixel in the image to a corresponding object or class.
The primary goal of semantic segmentation is to produce a pixel-wise dense segmentation map
The Importance of Semantic Similarity
When it comes to understanding the meaning of language, it's not just about individual words, phrases, or even sentences. The true meaning of language is found in the relationships between these linguistic elements. And that's where semantic similarity comes in. Semantic similarity is all about measuring the degree to which two or more pieces of language are similar in meaning. By using semantic similarity measures, we can gain deeper insights into the rela
Semantic SLAM is one of the newest trends in robotics, and it is a fascinating topic that has been recently developed. SLAM stands for Simultaneous Localization and Mapping, which is one of the most important procedures for Autonomous Robots or any other robotic devices. One of its primary objectives is to create an accurate map of the robotic environment.
What is SLAM?
Simultaneous Localization and Mapping (SLAM), is a process where a robot calculates its position in the environment while cr
Semanticity Prediction: Estimating the Meaning in Words Using Physiological Signals
Semanticity prediction is a fascinating study that seeks to establish a correlation between physiological signals and the perception of meaning in words. It involves the use of brain signals, such as EEG, GSP, and PPG, to classify words as either semantic or non-semantic. Essentially, the aim is to develop a model that can accurately predict the level of semanticity perceived by a listener.
Understanding Seman
Semi-Supervised Formality Style Transfer
Have you ever read an email or a text message from a colleague or friend that was too formal or too informal for the situation? Maybe it felt awkward or uncomfortable for you. The use of proper language and tone is important in different social and professional settings. Formality style transfer is a technique used to automatically adjust the formality of text to suit the intended style. Semi-Supervised Formality Style Transfer is a method for achieving
Overview of Semi-Supervised Knowledge Distillation (SSKD)
Semi-Supervised Knowledge Distillation (SSKD) is a special type of knowledge distillation that is used for person re-identification. It makes use of weakly annotated data to improve the ability of models to generalize. SSKD assigns soft pseudo labels to YouTube-Human to achieve this goal.
What is Person Re-Identification?
Person re-identification is a process that is used to identify people from images or videos taken from different c
Introduction:
Semi-Supervised Semantic Segmentation is a process which involves training machine learning models on a small set of labeled data, and then using a large set of unlabeled data to help the model to identify different objects, backgrounds, or contexts in an image. The objective of this process is to produce reliable and accurate segmentations for all the pixels in an image. This technology is being applied in a variety of fields, from medical diagnostics to self-driving cars.
The
Understanding Semi-Supervised Support Vector Machines: Definition, Explanations, Examples & Code
Semi-Supervised Support Vector Machines (S3VM) is an extension of Support Vector Machines (SVM) for semi-supervised learning. It is an instance-based algorithm that makes use of a large amount of unlabelled data and a small amount of labelled data to perform classification tasks. The aim is to leverage the unlabelled data to improve the decision boundary constructed from the labelled data alone, whi
Semi-Supervised Video Object Segmentation: What it is and How it Works
Semi-Supervised Video Object Segmentation is a process used to identify specific objects in a video sequence. By providing a full mask of the object(s) of interest in the first frame of a video sequence, the algorithm can identify and track the object(s) in subsequent frames. Using this method, users can quickly and accurately identify objects in video footage without the need for extensive manual input.
Why Use Semi-Super
SENet: Dynamic Channel-Wise Feature Recalibration
In the world of computer science, especially in the field of deep learning, artificial neural networks have become the backbone of various advanced technologies. A convolutional neural network (CNN) is a type of neural network that has revolutionized the field of image recognition. Researchers have been experimenting with various neural network architectures, aiming to achieve better and more accurate results.
SENet, or Squeeze-and-Excitation N
Sensor modeling involves creating mathematical models that represent the behavior of different sensors. These sensors can be used in a variety of applications such as cameras, LiDAR sensors, radar sensors, and more. The models are used to simulate the behavior of the sensors in different environments to predict how they will respond to certain conditions.
The Importance of Sensor Modeling
One of the main benefits of sensor modeling is that it allows engineers to design and test new sensor sys
Sentence Pair Modeling: What it is and why it matters?
Sentence pair modeling is a technique used in natural language processing to evaluate two sentences based on their internal representation. In simple words, it compares two sentences and helps determine their relationship. This technique is widely used in chatbots, search engines, and many other applications that involve natural language processing. Sentence pair modeling is a crucial concept in NLP, and its importance is increasing day by
SentencePiece is a tool used in natural language processing to segment words into smaller subunits, making it easier for machines to understand and analyze them. This makes it a useful tool in tasks such as language translation, sentiment analysis, and chatbots.
What is Subword Tokenization?
Subword tokenization refers to the process of breaking down words into smaller subunits or segments, called subwords. It is a useful technique when working with languages that have a large number of words
What is SepFormer for Speech Separation?
SepFormer is a neural network created to separate speech signals in a recording. It uses a transformer-based architecture that is designed to learn both short and long-term dependencies. The SepFormer is mainly composed of multi-head attention and feed-forward layers, and it adopts a dual-path framework introduced by the DPRNN to mitigate the quadratic complexity of transformers. It replaces RNNs with a multiscale pipeline composed of transformers to acc
Seq2Edits: An Open-Vocabulary Approach to Sequence Editing for NLP
Seq2Edits is a unique approach to natural language processing (NLP) that utilizes a sequence-to-sequence transduction represented as a series of edit operations. This open-vocabulary approach is used for tasks with a high overlap between input and output texts, such as text normalization, sentence fusion, sentence splitting & rephrasing, text simplification, and grammatical error correction. This method improves the explainabili
Seq2Seq, or Sequence to Sequence, is a model that is commonly used in sequence prediction tasks. This includes language modelling and machine translation. It uses a type of neural network called LSTM, which stands for Long Short-Term Memory. The first LSTM is called the encoder and its job is to read the input sequence one timestep at a time. This creates a large fixed dimensional vector representation called a context vector. The second LSTM is called the decoder and it uses the context vector