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A Study on Cross-Media Teaching Model for College English Classroom Based on Output-Driven Hypothetical Neural Network

Xiangyu Guo, Gengxin Sun
2022 Computational Intelligence and Neuroscience  
English teaching through traditional methods such as grammar-translation method and deductive method and constructs a new cross-media university English teaching model.  ...  The following research has been added to the abstract: to address the key problem of the semantic gap that is difficult to cross in cross-media semantic learning, a cross-media supervised adversarial hashing  ...  A supervised adversarial hashing algorithm based on two-way attentional features (as in Figure 3 ) is used to learn semantic associations and hash encoding across modal data.  ... 
doi:10.1155/2022/5283439 pmid:35586100 pmcid:PMC9110143 fatcat:3spnwxissndh3jpuvdu7uqq45a

Editorial: Privacy-Preserving Deep Heterogeneous View Perception for Data Learning

Peng Li
2022 Frontiers in Neurorobotics  
is increasingly attracting more attention.  ...  With the emergence of deep heterogeneous view perception, privacies hidden in data are becoming more easily leaked.  ...  The authors proposed a cross-modal method to realize context-awareness transfer in a few-shot image classification scene, which fully utilizes the information in heterogeneous data.  ... 
doi:10.3389/fnbot.2022.862535 pmid:35370595 pmcid:PMC8973699 fatcat:tixdpi47bjf7fbbxbwt4houxsq

Gaussian process decentralized data fusion meets transfer learning in large-scale distributed cooperative perception

Ruofei Ouyang, Bryan Kian Hsiang Low
2019 Autonomous Robots  
Networks Cross-Modal Hashing Zhenduo Chen, Wanjin Yu, Chuanxiang Li, Liqiang Nie, Xin-shun Xu* Dual Set Multi-Label Learning Chong Liu*, Peng Zhao, Sheng-Jun Huang, Yuan Jiang, Zhi-Hua Zhou Dual Transfer  ...  Flow for Multiple-choice Reading Comprehension Haichao Zhu, Furu Wei, Bing Qin*, Ting Liu Hierarchical Attention Transfer Network for Cross-domain Sentiment Classification Zheng Li*, Ying Wei, Yu Zhang  ... 
doi:10.1007/s10514-018-09826-z fatcat:67yqhwmgozccxni56rxmuapjgm

A Natural and Immersive Virtual Interface for the Surgical Safety Checklist Training

Andrea Ferracani, Daniele Pezzatini, Alberto Del Bimbo
2014 Proceedings of the 2014 ACM International Workshop on Serious Games - SeriousGames '14  
Kankanhalli Virtual Portaitist: Aesthetic Evaluation of Selfies Based on Angle Mei-Chen Yeh, Hsiao-Wei Lin Cross Modal Deep Model and Gaussian Process Based Model for MSR-Bing Challenge Jian Wang, Cuicui  ...  Pairwise Constraint Propagation Cross-media Hashing with Neural Networks How Your Portrait Impresses People?  ... 
doi:10.1145/2656719.2656725 dblp:conf/mm/FerracaniPB14a fatcat:obsb2i4iybhu3dq77hujvjtbze

Complete Issue Volume 3 Issue 1

Editor Journal of Teaching and Learning with Technology
2014 Journal of Teaching and Learning with Technology  
There is also a change in attitude towards teaching in which the attention is on the students rather than the teacher (ibid, 2012).  ...  Students are able to post real-time flowing discussion in a space using hash tags and content management.  ... 
doaj:5f82f9b8c683413eb87d7bba36f1b2d0 fatcat:iydbuflcdrcvvkevjeihiukwhq

DA-GAN: Dual Attention Generative Adversarial Network for Cross-Modal Retrieval

Liewu Cai, Lei Zhu, Hongyan Zhang, Xinghui Zhu
2022 Future Internet  
This technique is an adversarial semantic representation model with a dual attention mechanism, i.e., intra-modal attention and inter-modal attention.  ...  Cross-modal retrieval aims to search samples of one modality via queries of other modalities, which is a hot issue in the community of multimedia.  ...  representation of image I i the attention-aware representation of text T i the cross-modal common semantic representation of image I i the cross-modal common semantic representation of text T i the attention-aware  ... 
doi:10.3390/fi14020043 fatcat:s6alildx7zdrtc6u4rjlrdj6mm

Semantic-Aware Knowledge Preservation for Zero-Shot Sketch-Based Image Retrieval [article]

Qing Liu, Lingxi Xie, Huiyu Wang, Alan Yuille
2019 arXiv   pre-print
For this purpose, we design an approach named Semantic-Aware Knowledge prEservation (SAKE), which fine-tunes the pre-trained model in an economical way and leverages semantic information, e.g., inter-class  ...  To tackle this problem, we call for a model to simultaneously solve the problems of object recognition, cross-modal matching, and domain adaptation.  ...  The most important issue of this task lies in finding a shared feature embedding for cross-modality data, which requires mapping each sketch and photo image to a high-dimensional vector in the feature  ... 
arXiv:1904.03208v3 fatcat:ovs6hdklwnhanogtm7wdvsi5ay

2020 Index IEEE/ACM Transactions on Audio, Speech, and Language Processing Vol. 28

2020 IEEE/ACM Transactions on Audio Speech and Language Processing  
., +, TASLP 2020 185-197 How to Teach DNNs to Pay Attention to the Visual Modality in Speech Rec-Multichannel Non-Negative Matrix Factorization Using Banded Spatial Covariance Matrices in Wavenumber Domain  ...  Gribben, C., +, TASLP 2020 876-888 Deep learning On Cross-Corpus Generalization of Deep Learning Based Speech Enhancement.  ... 
doi:10.1109/taslp.2021.3055391 fatcat:7vmstynfqvaprgz6qy3ekinkt4

Fine-Grained Image Analysis with Deep Learning: A Survey [article]

Xiu-Shen Wei and Yi-Zhe Song and Oisin Mac Aodha and Jianxin Wu and Yuxin Peng and Jinhui Tang and Jian Yang and Serge Belongie
2021 arXiv   pre-print
Capitalizing on advances in deep learning, in recent years we have witnessed remarkable progress in deep learning powered FGIA.  ...  ., to teach machine to "see" in a fine-grained manner.  ...  [180] used pairedembeddings by employing cross-modal co-attention and hierarchical stroke/region-wise feature fusion in order to deal with varying levels of sketch detail.  ... 
arXiv:2111.06119v2 fatcat:ninawxsjtnf4lndtqquuwl3weq

What can autism teach us about the role of sensorimotor systems in higher cognition? New clues from studies on language, action semantics, and abstract emotional concept processing

Rachel L. Moseley, Friedemann Pulvermüller
2018 Cortex  
systems with cross-modal 'hubs' or 'convergence zones', a putative substrate of which may exist in anterior temporal lobe ( .  ...  We consider briefly, here, some avenues worthy of research attention.  ... 
doi:10.1016/j.cortex.2017.11.019 pmid:29306521 fatcat:zqu2kxopbrgixf7fuv2rgx44f4

Second order probabilistic models for within-document novelty detection in academic articles

Laurence A.F. Park, Simeon Simoff
2014 Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval - SIGIR '14  
Data Example and Tag Selection Qifan Wang, Luo Si, Zhiwei Zhang, Ning Zhang  Latent Semantic Sparse Hashing for Cross-Modal Similarity Search Jile Zhou, Guiguang Ding, Yuchen Guo SIGIR 2014 Poster  ...  (Room 7) Chair: Mark Sanderson  Discriminative Coupled Dictionary Hashing for Fast Cross-Media Retrieval Zhou Yu, Fei Wu, Yi Yang, Qi Tian, Jiebo Luo, Yueting Zhuang  Active Hashing with Joint  ... 
doi:10.1145/2600428.2609520 dblp:conf/sigir/ParkS14 fatcat:ye2rtri2xjbyrjvkkzgt7srcfu

Generative Models for Novelty Detection: Applications in abnormal event and situational change detection from data series [article]

Mahdyar Ravanbakhsh
2019 arXiv   pre-print
To model the PL of self-awareness, a set of cross-modal GANs [46] is trained to learn the normality from this set of visual data.  ...  Such network potentially provides us with a robust self-aware model through a cross-correlation between the layers using multi-modal DBNs.  ... 
arXiv:1904.04741v1 fatcat:fdwhsuaoi5hcdbjzcbjh2z6ydu

A Survey on Graph-Based Deep Learning for Computational Histopathology [article]

David Ahmedt-Aristizabal, Mohammad Ali Armin, Simon Denman, Clinton Fookes, Lars Petersson
2021 arXiv   pre-print
As such, graph data representations and deep learning have attracted significant attention for encoding tissue representations, and capturing intra- and inter- entity level interactions.  ...  With the remarkable success of representation learning for prediction problems, we have witnessed a rapid expansion of the use of machine learning and deep learning for the analysis of digital pathology  ...  Adaptation of deep learning from images to graphs has received increased attention, leading to a new cross-domain field of graph-based deep learning which seeks to learn informative representations of  ... 
arXiv:2107.00272v2 fatcat:3eskkeref5ccniqsjgo3hqv2sa

DEEP LEARNING FOR SEMANTIC MATCHING: A SURVEY

Han Li, Yash Govind, Sidharth Mudgal, Theodoros Rekatsinas, AnHai Doan
2021 Journal of Computer Science and Cybernetics  
Semantic matching has received much attention in the database, AI, KDD, Web, and Semantic Web communities. Recently, many works have also applied deep learning (DL) to semantic matching.  ...  Semantic-aware word embeddings. Deep learning models have limited capability of capturing domain-specific semantics.  ...  The works [88, 103] examine cross-lingual entity linking using deep learning.  ... 
doi:10.15625/1813-9663/37/4/16151 fatcat:apdgcuwds5cbnanalqwzur2g5m

A Systematic Review on Affective Computing: Emotion Models, Databases, and Recent Advances [article]

Yan Wang, Wei Song, Wei Tao, Antonio Liotta, Dawei Yang, Xinlei Li, Shuyong Gao, Yixuan Sun, Weifeng Ge, Wei Zhang, Wenqiang Zhang
2022 arXiv   pre-print
Affective computing plays a key role in human-computer interactions, entertainment, teaching, safe driving, and multimedia integration.  ...  Zhao et al [364] proposed a novel deep visual-audio attention network (VAANet) with specific attention modules and polarity-consistent cross-entropy loss.  ...  On the basis of the work [224] , sparsity-aware deep learning [282] was further proposed to compute the sparse representations of multi-view CNN features.  ... 
arXiv:2203.06935v3 fatcat:h4t3omkzjvcejn2kpvxns7n2qe
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