scholarly journals Few-Shot Intent Classification by Gauging Entailment Relationship Between Utterance and Semantic Label

2021 ◽  
Author(s):  
Jin Qu ◽  
Kazuma Hashimoto ◽  
Wenhao Liu ◽  
Caiming Xiong ◽  
Yingbo Zhou
Keyword(s):  
Semantic Label

scholarly journals Cross-View Local Structure Preserved Diversity and Consensus Learning for Multi-View Unsupervised Feature Selection

2019 ◽  
Vol 33 ◽  
pp. 5101-5108 ◽  
Author(s):  
Chang Tang ◽  
Xinzhong Zhu ◽  
Xinwang Liu ◽  
Lizhe Wang
Keyword(s):  
Feature Selection ◽  
Local Structure ◽  
Feature Subset ◽  
Unsupervised Feature Selection ◽  
Semantic Learning ◽  
Common Information ◽  
Semantic Label ◽  
Similarity Graph ◽  
Feature Projection ◽  
Norm Constraint

Multi-view unsupervised feature selection (MV-UFS) aims to select a feature subset from multi-view data without using the labels of samples. However, we observe that existing MV-UFS algorithms do not well consider the local structure of cross views and the diversity of different views, which could adversely affect the performance of subsequent learning tasks. In this paper, we propose a cross-view local structure preserved diversity and consensus semantic learning model for MV-UFS, termed CRV-DCL briefly, to address these issues. Specifically, we project each view of data into a common semantic label space which is composed of a consensus part and a diversity part, with the aim to capture both the common information and distinguishing knowledge across different views. Further, an inter-view similarity graph between each pairwise view and an intra-view similarity graph of each view are respectively constructed to preserve the local structure of data in different views and different samples in the same view. An l2,1-norm constraint is imposed on the feature projection matrix to select discriminative features. We carefully design an efficient algorithm with convergence guarantee to solve the resultant optimization problem. Extensive experimental study is conducted on six publicly real multi-view datasets and the experimental results well demonstrate the effectiveness of CRV-DCL.

scholarly journals Cross-Modality Attention with Semantic Graph Embedding for Multi-Label Classification

2020 ◽  
Vol 34 (07) ◽  
pp. 12709-12716
Author(s):  
Renchun You ◽  
Zhiyao Guo ◽  
Lei Cui ◽  
Xiang Long ◽  
Yingze Bao ◽  
...  
Keyword(s):  
Computer Vision ◽  
Graph Embedding ◽  
Video Classification ◽  
Label Graph ◽  
Generalization Capability ◽  
Semantic Graph ◽  
The Arts ◽  
Challenging Tasks ◽  
Semantic Label ◽  
Similarity Graph

Multi-label image and video classification are fundamental yet challenging tasks in computer vision. The main challenges lie in capturing spatial or temporal dependencies between labels and discovering the locations of discriminative features for each class. In order to overcome these challenges, we propose to use cross-modality attention with semantic graph embedding for multi-label classification. Based on the constructed label graph, we propose an adjacency-based similarity graph embedding method to learn semantic label embeddings, which explicitly exploit label relationships. Then our novel cross-modality attention maps are generated with the guidance of learned label embeddings. Experiments on two multi-label image classification datasets (MS-COCO and NUS-WIDE) show our method outperforms other existing state-of-the-arts. In addition, we validate our method on a large multi-label video classification dataset (YouTube-8M Segments) and the evaluation results demonstrate the generalization capability of our method.

scholarly journals The Visual Features of Emotional Faces That Predict Forced Choice Selection of Faces Download Text Copy to Clipboard

2020 ◽  
Author(s):  
Sjoerd Stuit ◽  
Timo Kootstra ◽  
David Terburg ◽  
Carlijn van den Boomen ◽  
Maarten van der Smagt ◽  
...  
Keyword(s):  
Facial Expressions ◽  
Visual Features ◽  
Emotional Expressions ◽  
Emotional Facial Expressions ◽  
Communication Signals ◽  
Machine Learning Classification ◽  
Histograms Of Oriented Gradients ◽  
Semantic Label ◽  
Face Stimuli

Abstract Emotional facial expressions are important visual communication signals that indicate a sender’s intent and emotional state to an observer. As such, it is not surprising that reactions to different expressions are thought to be automatic and independent of awareness. What is surprising, is that studies show inconsistent results concerning such automatic reactions, particularly when using different face stimuli. We argue that automatic reactions to facial expressions can be better explained, and better understood, in terms of quantitative descriptions of their visual features rather than in terms of the semantic labels (e.g. angry) of the expressions. Here, we focused on overall spatial frequency (SF) and localized Histograms of Oriented Gradients (HOG) features. We used machine learning classification to reveal the SF and HOG features that are sufficient for classification of the first selected face out of two simultaneously presented faces. In other words, we show which visual features predict selection between two faces. Interestingly, the identified features serve as better predictors than the semantic label of the expressions. We therefore propose that our modelling approach can further specify which visual features drive the behavioural effects related to emotional expressions, which can help solve the inconsistencies found in this line of research.

scholarly journals Class-Wise Fully Convolutional Network for Semantic Segmentation of Remote Sensing Images

2021 ◽  
Vol 13 (16) ◽  
pp. 3211
Author(s):  
Tian Tian ◽  
Zhengquan Chu ◽  
Qian Hu ◽  
Li Ma
Keyword(s):  
Remote Sensing ◽  
Image Interpretation ◽  
Semantic Segmentation ◽  
Remote Sensing Images ◽  
Feature Maps ◽  
Convolutional Network ◽  
Fully Convolutional Network ◽  
Semantic Labeling ◽  
Benchmark Datasets ◽  
Semantic Label

Semantic segmentation is a fundamental task in remote sensing image interpretation, which aims to assign a semantic label for every pixel in the given image. Accurate semantic segmentation is still challenging due to the complex distributions of various ground objects. With the development of deep learning, a series of segmentation networks represented by fully convolutional network (FCN) has made remarkable progress on this problem, but the segmentation accuracy is still far from expectations. This paper focuses on the importance of class-specific features of different land cover objects, and presents a novel end-to-end class-wise processing framework for segmentation. The proposed class-wise FCN (C-FCN) is shaped in the form of an encoder-decoder structure with skip-connections, in which the encoder is shared to produce general features for all categories and the decoder is class-wise to process class-specific features. To be detailed, class-wise transition (CT), class-wise up-sampling (CU), class-wise supervision (CS), and class-wise classification (CC) modules are designed to achieve the class-wise transfer, recover the resolution of class-wise feature maps, bridge the encoder and modified decoder, and implement class-wise classifications, respectively. Class-wise and group convolutions are adopted in the architecture with regard to the control of parameter numbers. The method is tested on the public ISPRS 2D semantic labeling benchmark datasets. Experimental results show that the proposed C-FCN significantly improves the segmentation performances compared with many state-of-the-art FCN-based networks, revealing its potentials on accurate segmentation of complex remote sensing images.

scholarly journals A Two-Stage GAN for High-Resolution Retinal Image Generation and Segmentation

Electronics ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 60
Author(s):  
Paolo Andreini ◽  
Giorgio Ciano ◽  
Simone Bonechi ◽  
Caterina Graziani ◽  
Veronica Lachi ◽  
...  
Keyword(s):  
High Resolution ◽  
Retinal Vessel ◽  
Real Data ◽  
Generative Adversarial Networks ◽  
Retinal Images ◽  
Two Stage ◽  
Generation Task ◽  
Training Samples ◽  
Vessel Structure ◽  
Semantic Label

In this paper, we use Generative Adversarial Networks (GANs) to synthesize high-quality retinal images along with the corresponding semantic label-maps, instead of real images during training of a segmentation network. Different from other previous proposals, we employ a two-step approach: first, a progressively growing GAN is trained to generate the semantic label-maps, which describes the blood vessel structure (i.e., the vasculature); second, an image-to-image translation approach is used to obtain realistic retinal images from the generated vasculature. The adoption of a two-stage process simplifies the generation task, so that the network training requires fewer images with consequent lower memory usage. Moreover, learning is effective, and with only a handful of training samples, our approach generates realistic high-resolution images, which can be successfully used to enlarge small available datasets. Comparable results were obtained by employing only synthetic images in place of real data during training. The practical viability of the proposed approach was demonstrated on two well-established benchmark sets for retinal vessel segmentation—both containing a very small number of training samples—obtaining better performance with respect to state-of-the-art techniques.

scholarly journals Device-Oriented Automatic Semantic Annotation in IoT

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Fagui Liu ◽  
Ping Li ◽  
Dacheng Deng
Keyword(s):  
Information Integration ◽  
Semantic Annotation ◽  
Semantic Technologies ◽  
Baseline Method ◽  
Annotation Method ◽  
Information Interaction ◽  
Classification Property ◽  
Semantic Label ◽  
Iot Devices ◽  
The Internet Of Things

Semantic technologies are the keys to address the problem of information interaction between assorted, heterogeneous, and distributed devices in the Internet of Things (IoT). Semantic annotation of IoT devices is the foundation of IoT semantics. However, the large amount of devices has led to the inadequacy of the manual semantic annotation and stressed the urgency into the research of automatic semantic annotation. To overcome these limitations, a device-oriented automatic semantic annotation method is proposed to annotate IoT devices’ information. The processes and corresponding algorithms of the automatic semantic annotation method are presented in detail, including the information extraction, text classification, property information division, semantic label selection, and information integration. Experiments show that our method is effective for the automatic semantic annotation to IoT devices’ information. In addition, compared to a typical rule-based method, the comparison experiment demonstrates that our approach outperforms this baseline method with respect to the precision and F-measure.

scholarly journals Enhancing argumentation component classification using contextual language model

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Hidayaturrahman ◽  
Emmanuel Dave ◽  
Derwin Suhartono ◽  
Aniati Murni Arymurthy
Keyword(s):  
Language Processing ◽  
Language Model ◽  
Language Models ◽  
Context Sensitive ◽  
Argumentation Mining ◽  
Prediction Time ◽  
Semantic Label ◽  
Core Idea ◽  
Increase In Accuracy ◽  
Context Free

AbstractArguments facilitate humans to deliver their ideas. The outcome of the discussion heavily relies on the validity of the argument. If an argument is well-composed, it is more effective to grasp the core idea behind the argument. To grade the argument, machines can be utilized by decomposing into semantic label components. In natural language processing, multiple language models are available to perform this task. It is divided into context-free and contextual models. The majority of previous studies used hand-crafted features to perform argument component classification, while state of the art language models utilize machine learning. The majority of these language models ignore the context in an argument. This research paper aims to analyze whether by including the context in the classification process may improve the accuracy of the language model which will enhance the argumentation mining process as well. The same document corpus is fed into several language models. Word2Vec and GLoVe represent the context free models, while BERT and ELMo as context sensitive language models. Accuracy and time from each model are then compared to determine the importance of context. The result shows that contextual language models are proven to be able to boost classification accuracy by approximately 20%. However, time comes as a cost where contextual models require longer training and prediction time. The benefit from the increase in accuracy outweighs the burden of time. Thus, as a contextual task, argumentation mining is suggested to use contextual model where context must be included to achieve promising results.

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