Enhancing semantic image retrieval with limited labeled examples via deep learning

Xu, Haijiao; Huang, Changqin; Wang, Dianhui

doi:10.1016/j.knosys.2018.08.032

Cited by 24 publications

(6 citation statements)

References 35 publications

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“…This includes playing Go or videogames [1], speech and NLP [2], object and facial recognition [3]. With reference to natural language processing, the use of deep learning models in the last five years has strongly propelled it forward, with considerable advances in real-world NLP applications, like image captioning [4,5], visual question answering [6,7], web search and information retrieval [8,9], sentiment analysis [10,11] and recommender systems [12,13]. Architectures inspired by human cognition have been proposed to model language comprehension, learning and reasoning.…”

Section: Introductionmentioning

confidence: 99%

Modeling Multiple Language Learning in a Developmental Cognitive Architecture

Giorgi

Golosio

Esposito

et al. 2021

IEEE Trans. Cogn. Dev. Syst.

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In this work, we model multiple natural language learning in a developmental neuroscience-inspired architecture. The ANNABELL model (Artificial Neural Network with Adaptive Behaviour Exploited for Language Learning), is a large-scale neural network, however, unlike most deep learning methods that solve natural language processing (NLP) tasks, it does not represent an empirical engineering solution for specific NLP problems; rather, its organisation complies with findings from cognitive neuroscience, particularly the multi-compartment working memory models. The system is appropriately trained to understand the level of cognitive development required for language acquisition and the robustness achieved in learning simultaneously four languages, using a corpus of text-based exchanges of developmental complexity. The selected languages, Greek, Italian and Albanian, besides English, differ significantly in structure and complexity. Initially, the system was validated in each language alone and was then compared with the open-ended cumulative training, in which languages are learned jointly, prior to querying with random language at random order. We aimed to assess if the model could learn the languages together to the same degree of skill as learning each apart. Moreover, we explored the generalisation skill in multilingual context questions and the ability to elaborate a short text of preschool literature. We verified if the system could follow a dialogue coherently and cohesively, keeping track of its previous answers and recalling them in subsequent queries. The results show that the architecture developed broad language processing functionalities, with satisfactory performances in each language trained singularly, maintaining high accuracies when they are acquired cumulatively.

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Section: Introductionmentioning

confidence: 99%

Modeling Multiple Language Learning in a Developmental Cognitive Architecture

Giorgi

Golosio

Esposito

et al. 2021

IEEE Trans. Cogn. Dev. Syst.

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“…Different from the traditional method of using multiple independent concepts in multiconcept semantic query, the proposed method regarded multiple concepts as a whole scene, which was used for multiconcept scene learning of unimodal retrieval. The comprehensive experimental results on two datasets of MIR flickr2011 and NUS-WIDE indicated that the proposed method was superior to some of the latest methods [ 23 ]. Long and Zhao held that intelligent teaching mode overcame the shortcomings of traditional online and offline teaching.…”

Section: Literature Reviewmentioning

confidence: 99%

The Fusion Application of Deep Learning Biological Image Visualization Technology and Human-Computer Interaction Intelligent Robot in Dance Movements

Jin

Wen

Xie

2022

Computational Intelligence and Neuroscience

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The paper aims to apply the deep learning-based image visualization technology to extract, recognize, and analyze human skeleton movements and evaluate the effect of the deep learning-based human-computer interaction (HCI) system. Dance education is researched. Firstly, the Visual Geometry Group Network (VGGNet) is optimized using Convolutional Neural Network (CNN). Then, the VGGNet extracts the human skeleton movements in the OpenPose database. Secondly, the Long Short-Term Memory (LSTM) network is optimized and recognizes human skeleton movements. Finally, an HCI system for dance education is designed based on the extraction and recognition methods of human skeleton movements. Results demonstrate that the highest extraction accuracy is 96%, and the average recognition accuracy of different dance movements is stable. The effectiveness of the proposed model is verified. The recognition accuracy of the optimized F-Multiple LSTMs is increased to 88.9%, suitable for recognizing human skeleton movements. The dance education HCI system’s interactive accuracy built by deep learning-based visualization technology reaches 92%; the overall response time is distributed between 5.1 s and 5.9 s. Hence, the proposed model has excellent instantaneity. Therefore, the deep learning-based image visualization technology has enormous potential in human movement recognition, and combining deep learning and HCI plays a significant role.

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“…e parameter values can be adjusted during the actual experiment, as shown in Figure 1. e initial clustering of the KCA is Gaussian filtering to build a pyramid [13].…”

Section: K-means Clustering Algorithmmentioning

confidence: 99%

Computer Image Content Retrieval considering K-Means Clustering Algorithm

Liu

2022

Mathematical Problems in Engineering

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The traditional computer image content retrieval technology can only meet the specific requirements of customers; because of its general features, it cannot comply with the requirements of various environments, purposes, and time simultaneously. This study presents a computer image content retrieval method for a K-means clustering algorithm (KCA). The information collected by computer is preprocessed by K-means clustering algorithm, and the unacquired computer image is labeled based on the optimal learning order according to the KCA. The K-means clustering algorithm classifies the color, pattern, shape, and content of computer images and takes advantage of the invariance advantages of image content retrieval such as scale, rotation, illumination, and blur correction to effectively solve the recurring problems of computer images during retrieval and increase its accuracy. The results of the experiment indicate that the proposed K-means clustering algorithm can enhance the efficiency and performance effectively during image retrieval by computer as compared to the traditional content search algorithm and also help to quickly converge to the query content; it also shows that KCA has good robustness.

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Enhancing semantic image retrieval with limited labeled examples via deep learning

Cited by 24 publications

References 35 publications

Modeling Multiple Language Learning in a Developmental Cognitive Architecture

Modeling Multiple Language Learning in a Developmental Cognitive Architecture

The Fusion Application of Deep Learning Biological Image Visualization Technology and Human-Computer Interaction Intelligent Robot in Dance Movements

Computer Image Content Retrieval considering K-Means Clustering Algorithm

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