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Guest editorial: web multimedia semantic inference using multi-cues
2015
World wide web (Bussum)
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The paper, titled "A Cross-media Distance Metric Learning Framework based on Multi-view Correlation Mining and Matching", presents a novel cross-media distance metric learning framework based on sparse ...
Cross-media distance metric learning focuses on correlation measure between multimedia data of different modalities. ...
doi:10.1007/s11280-015-0360-2
fatcat:vc4plge5qvg7hfmza3dffmawki
2018 Index IEEE Transactions on Knowledge and Data Engineering Vol. 30
2019
IEEE Transactions on Knowledge and Data Engineering
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., þ, TKDE April 2018 717-728 Learning (artificial intelligence) A Comprehensive Study on Social Network Mental Disorders Detection via Online Social Media Mining. ...
., þ, TKDE Nov. 2018 2185-2198 Paradoxical Correlation Pattern Mining. Zhou, W., þ, TKDE Aug. 2018 1561-1574 SLA Definition for Multi-Tenant DBMS and its Impact on Query Optimization. ...
doi:10.1109/tkde.2018.2882359
fatcat:asiids266jagrkx5eac6higrlq
Multi-modal Deep Analysis for Multimedia
2019
IEEE transactions on circuits and systems for video technology (Print)
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2019 pre-print arXiv:1910.04964v1 fatcat:76yma7ca4fhajduyuyuos3csziOther Versions
More specifically, on data-driven correlational representation, we highlight three important categories of methods, such as multi-modal deep representation, multi-modal transfer learning, and multi-modal ...
answering, multi-modal video summarization, multi-modal visual pattern mining and multi-modal recommendation. ...
ACKNOWLEDGMENT We thank Guohao Li, Shengze Yu and Yitian Yuan for providing relevant materials and valuable opinions. This work will never be accomplished without their useful suggestions. ...
doi:10.1109/tcsvt.2019.2940647
fatcat:l4tchrkgrnaeradvc4nhfan2w4
D-Sempre: Learning Deep Semantic-Preserving Embeddings for User interests-Social Contents Modeling
[article]
2018
arXiv
pre-print
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As a result, D-Sempre effectively integrates the multi-modal data from heterogeneous social media feeds and captures the hidden semantic correlations between users' interests and social contents. ...
At last, a Deep Semantic-Preserving Embedding (D-Sempre) is learned, and the ranking results can be given by calculating distances between social contents and users. ...
term for the cross-view matching task. • We point out the importance of visual content, external social context and social relation. ...
arXiv:1802.06451v1
fatcat:kzjmdotq2bgffiplfhxlu3qldm
Joint graph regularization based semantic analysis for cross-media retrieval: a systematic review
2018
International Journal of Engineering & Technology
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Many research is done on the cross-media retrieval with different methods and provide the different result. ...
If the query is given as the text and acquired result are present in text. The users demand the cross-media retrieval for their queries and it is very consistent in providing the result. ...
[11] , proposed the model for the cross-media uniform representation and also done the cross-media correlation with understanding and deep mining. ...
doi:10.14419/ijet.v7i2.7.10592
fatcat:6quy3kb2g5dhvg2ccv2vppg7be
Adversarial Attentive Multi-modal Embedding Learning for Image-Text Matching
2020
IEEE Access
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Mining the correlation between image and text to learn effective multi-modal features is crucial for image-text matching. ...
In this work, we propose a novel model named Adversarial Attentive Multi-modal Embedding Learning (AAMEL) for image-text matching. ...
Mining this type of correlation is useful to learn more effective multi-modal representation for image-text matching. ...
doi:10.1109/access.2020.2996407
fatcat:ilgrbt4k2zebbe3nlf532c3jma
Cross-media Multi-level Alignment with Relation Attention Network
[article]
2018
arXiv
pre-print
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Second, we propose cross-media multi-level alignment to explore global, local and relation alignments across different media types, which can mutually boost to learn more precise cross-media correlation ...
Relation understanding is essential for cross-media correlation learning, which is ignored by prior cross-media retrieval works. ...
Acknowledgments This work was supported by National Natural Science Foundation of China under Grant 61771025 and Grant 61532005. ...
arXiv:1804.09539v1
fatcat:7bpfoixw2rbfji3b4h7jytyvly
Mining and searching association relation of scientific papers based on deep learning
[article]
2022
arXiv
pre-print
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Therefore, the research on mining and searching the association relationship of scientific papers based on deep learning has far-reaching practical significance. ...
There is a complex correlation among the data of scientific papers. ...
ACM-GCN [18] proposes a new metric based on similarity matrix, considering the influence of graph structure and input features on GNN, and then proposes an Adaptive Channel Mixing (ACM) framework to ...
arXiv:2204.11488v1
fatcat:zxwvpnids5bopberzumjofgupq
Twitter100k: A Real-world Dataset for Weakly Supervised Cross-Media Retrieval
[article]
2017
arXiv
pre-print
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As a minor contribution, inspired by the characteristic of Twitter100k, we propose an OCR-based cross-media retrieval method. ...
This paper contributes a new large-scale dataset for weakly supervised cross-media retrieval, named Twitter100k. ...
Deep learning methods. Some cross-media retrieval methods are based on deep learning. ...
arXiv:1703.06618v1
fatcat:qhvet57kmjhs7hcbcc2nslegfy
Deep Coordinated Textual and Visual Network for Sentiment-Oriented Cross-Modal Retrieval
[chapter]
2018
Lecture Notes in Computer Science
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The visual branch is based on a convolutional neural network (CNN) pre-trained on a large dataset, which is optimized with the classification loss. ...
This paper proposes a deep coordinated textural and visual network with two branches to learn a joint semantic embedding space for both images and texts. ...
U1533104), and the Open Project Program of the National Laboratory of Pattern Recognition (NLPR). ...
doi:10.1007/978-3-319-97304-3_52
fatcat:sqqwb5u7ivhe3k6jyf3si6mjj4
MULTI-MODAL RETRIEVAL IN NEWS FEED APP USING GCDL TECHNIQUE
2017
International Journal of Recent Trends in Engineering and Research
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sparse hashing (LSSH), discriminative coupled dictionary hashing (DCDH) , Cross-view Hashing (CVH), and so on. ...
Since each modality having different representation methods and correlational structures, a variety of methods studied the problem from the aspect of learning correlations between different modalities. ...
across the views to enable cross-view similarity search. Discriminative coupled dictionary hashing generates a coupled dictionary for each modality based on category labels. Multi view discriminative ...
doi:10.23883/ijrter.2017.3365.aeikk
fatcat:6dmfmfsmtbaejale6t63ts7may
The Re-identification Challenge
[chapter]
2014
Person Re-Identification
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As a result, the field has drawn growing and wide interest from academic researchers and industrial developers. ...
to solving some of the fundamental challenges in person re-identification, benefiting from research in computer vision, pattern recognition and machine learning, and drawing insights from video analytics ...
Learning Distance Metric A popular alternative to colour transformation learning is distance metric learning. ...
doi:10.1007/978-1-4471-6296-4_1
dblp:series/acvpr/GongCLH14
fatcat:l7jbhs4g2jgcxdjceywbszb374
Deep Learning Techniques for Future Intelligent Cross-Media Retrieval
[article]
2020
arXiv
pre-print
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In this paper, we provide a novel taxonomy according to the challenges faced by multi-modal deep learning approaches in solving cross-media retrieval, namely: representation, alignment, and translation ...
These challenges are evaluated on deep learning (DL) based methods, which are categorized into four main groups: 1) unsupervised methods, 2) supervised methods, 3) pairwise based methods, and 4) rank based ...
These methods enforce some constraints on alignment, such as the temporal ordering of sequences and similarity metric existence between the modalities. To align multi-view time series Kruskal et al. ...
arXiv:2008.01191v1
fatcat:t63bg55w2vdqjcprzaaidrmprq
User-curated image collections: Modeling and recommendation
2015
2015 IEEE International Conference on Big Data (Big Data)
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2015 pre-print arXiv:1509.05671v1 fatcat:sv4rommipbc2tjsip4iiu5uuhu
For image collection modeling, we consider each image collection as a whole in a group sparse reconstruction framework and extract concise collection descriptors given the pretrained dictionaries. ...
We then consider image collection recommendation as a dynamic similarity measurement problem in response to user's clicked image set, and employ a metric learner to measure the similarity between the image ...
recommendation based on metric learning. ...
doi:10.1109/bigdata.2015.7363803
dblp:conf/bigdataconf/LiMCL15
fatcat:ebw6hirf6jbz7iqdrzp5dhzysy
Multi-Face: Self-supervised Multiview Adaptation for Robust Face Clustering in Videos
[article]
2020
arXiv
pre-print
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We propose a nearest-neighbor search in the embedding space to mine hard examples from the face tracks followed by domain adaptation using multiview shared subspace learning. ...
Robust face clustering is a key step towards computational understanding of visual character portrayals in media. ...
We used the model trained on the VggFace2. SphereFace [24] is a metric-learning method which combines the ideas of cross-entropy and angular margin loss to improve classification. ...
arXiv:2008.11289v1
fatcat:mjmo66psm5ggxbebaacx4f525y
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