scholarly journals SAIF: A Correction-Detection Deep-Learning Architecture for Personal Assistants

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5577
Author(s):  
Amos Azaria ◽  
Keren Nivasch
Keyword(s):  
Deep Learning ◽  
Natural Language ◽  
Intelligent Agents ◽  
State Of The Art ◽  
Intelligent Agent ◽  
Current State ◽  
The Future ◽  
Art Methods ◽  
Personal Assistants ◽  
Unique Dataset

Intelligent agents that can interact with users using natural language are becoming increasingly common. Sometimes an intelligent agent may not correctly understand a user command or may not perform it properly. In such cases, the user might try a second time by giving the agent another, slightly different command. Giving an agent the ability to detect such user corrections might help it fix its own mistakes and avoid making them in the future. In this work, we consider the problem of automatically detecting user corrections using deep learning. We develop a multimodal architecture called SAIF, which detects such user corrections, taking as inputs the user’s voice commands as well as their transcripts. Voice inputs allow SAIF to take advantage of sound cues, such as tone, speed, and word emphasis. In addition to sound cues, our model uses transcripts to determine whether a command is a correction to the previous command. Our model also obtains internal input from the agent, indicating whether the previous command was executed successfully or not. Finally, we release a unique dataset in which users interacted with an intelligent agent assistant, by giving it commands. This dataset includes labels on pairs of consecutive commands, which indicate whether the latter command is in fact a correction of the former command. We show that SAIF outperforms current state-of-the-art methods on this dataset.

scholarly journals A Controlled Benchmark of Video Violence Detection Techniques

Information ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 321
Author(s):  
Nicola Convertini ◽  
Vincenzo Dentamaro ◽  
Donato Impedovo ◽  
Giuseppe Pirlo ◽  
Lucia Sarcinella
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Detection Techniques ◽  
Detection Systems ◽  
Current State ◽  
Violence Detection ◽  
Learning Techniques ◽  
The Future ◽  
Feature Based ◽  
Art Techniques

This benchmarking study aims to examine and discuss the current state-of-the-art techniques for in-video violence detection, and also provide benchmarking results as a reference for the future accuracy baseline of violence detection systems. In this paper, the authors review 11 techniques for in-video violence detection. They re-implement five carefully chosen state-of-the-art techniques over three different and publicly available violence datasets, using several classifiers, all in the same conditions. The main contribution of this work is to compare feature-based violence detection techniques and modern deep-learning techniques, such as Inception V3.

scholarly journals A Survey of Graphical Page Object Detection with Deep Neural Networks

2021 ◽  
Author(s):  
Jwalin Bhatt ◽  
Khurram Azeem Hashmi ◽  
Muhammad Zeshan Afzal ◽  
Didier Stricker
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Conceptual Understanding ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
Learning Approaches ◽  
Document Images ◽  
Essential Information ◽  
Current State ◽  
High Level

In any document, graphical elements like tables, figures, and formulas contain essential information. The processing and interpretation of such information require specialized algorithms. Off-the-shelf OCR components cannot process this information reliably. Therefore, an essential step in document analysis pipelines is to detect these graphical components. It leads to a high-level conceptual understanding of the documents that makes digitization of documents viable. Since the advent of deep learning, the performance of deep learning-based object detection has improved many folds. In this work, we outline and summarize the deep learning approaches for detecting graphical page objects in the document images. Therefore, we discuss the most relevant deep learning-based approaches and state-of-the-art graphical page object detection in document images. This work provides a comprehensive understanding of the current state-of-the-art and related challenges. Furthermore, we discuss leading datasets along with the quantitative evaluation. Moreover, it discusses briefly the promising directions that can be utilized for further improvements.

scholarly journals A Robust Context-Based Deep Learning Approach for Highly Imbalanced Hyperspectral Classification

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Juan F. Ramirez Rochac ◽  
Nian Zhang ◽  
Lara A. Thompson ◽  
Tolessa Deksissa
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Mineral Exploration ◽  
Classification Models ◽  
Noise Resistance ◽  
Deep Convolutional Neural Networks ◽  
Current State ◽  
Feature Augmentation ◽  
Active Research ◽  
Hyperspectral Classification

Hyperspectral imaging is an area of active research with many applications in remote sensing, mineral exploration, and environmental monitoring. Deep learning and, in particular, convolution-based approaches are the current state-of-the-art classification models. However, in the presence of noisy hyperspectral datasets, these deep convolutional neural networks underperform. In this paper, we proposed a feature augmentation approach to increase noise resistance in imbalanced hyperspectral classification. Our method calculates context-based features, and it uses a deep convolutional neuronet (DCN). We tested our proposed approach on the Pavia datasets and compared three models, DCN, PCA + DCN, and our context-based DCN, using the original datasets and the datasets plus noise. Our experimental results show that DCN and PCA + DCN perform well on the original datasets but not on the noisy datasets. Our robust context-based DCN was able to outperform others in the presence of noise and was able to maintain a comparable classification accuracy on clean hyperspectral images.

scholarly journals Speech analysis for health: Current state-of-the-art and the increasing impact of deep learning

Methods ◽  
2018 ◽  
Vol 151 ◽  
pp. 41-54 ◽  
Author(s):  
Nicholas Cummins ◽  
Alice Baird ◽  
Björn W. Schuller
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Speech Analysis ◽  
Current State

scholarly journals The future of multimodal corpora

2011 ◽  
Vol 11 (2) ◽  
pp. 391-415 ◽  
Author(s):  
Dawn Knight
Keyword(s):  
Corpus Linguistics ◽  
State Of The Art ◽  
Future Developments ◽  
The Past ◽  
Current State ◽  
Linguistic Research ◽  
Multimodal Corpora ◽  
The Future ◽  
Multimodal Corpus ◽  
Critical Overview

This paper takes stock of the current state-of-the-art in multimodal corpus linguistics, and proposes some projections of future developments in this field. It provides a critical overview of key multimodal corpora that have been constructed over the past decade and presents a wish-list of future technological and methodological advancements that may help to increase the availability, utility and functionality of such corpora for linguistic research.

Sign in / Sign up

Export Citation Format

Share Document