Gao, L.L.[Lian-Li],
Guo, Z.[Zhao],
Zhang, H.W.[Han-Wang],
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Shen, H.T.[Heng Tao],
Video Captioning With Attention-Based LSTM and Semantic Consistency,
MultMed(19), No. 9, September 2017, pp. 2045-2055.
IEEE DOI
1708
Computational modeling, Correlation, Feature extraction,
Neural networks, Semantics,
Visualization, Attention mechanism, embedding,
long short-term memory (LSTM), video, captioning
BibRef
Bin, Y.,
Yang, Y.,
Shen, F.,
Xie, N.,
Shen, H.T.,
Li, X.,
Describing Video With Attention-Based Bidirectional LSTM,
Cyber(49), No. 7, July 2019, pp. 2631-2641.
IEEE DOI
1905
Visualization, Semantics, Decoding, Feature extraction,
Natural languages, Recurrent neural networks, Grammar, video captioning
BibRef
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Cui, R.P.[Run-Peng],
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Aligning Where to See and What to Tell: Image Captioning with
Region-Based Attention and Scene-Specific Contexts,
PAMI(39), No. 12, December 2017, pp. 2321-2334.
IEEE DOI
1711
Adaptation models, Computational modeling, Context modeling,
Data mining, Feature extraction, Image classification,
Visualization, Image captioning, LSTM,
visual attention.
BibRef
Xiao, C.M.[Chang-Ming],
Yang, Q.[Qi],
Xu, X.Q.[Xiao-Qiang],
Zhang, J.W.[Jian-Wei],
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Zhang, C.S.[Chang-Shui],
Where you edit is what you get: Text-guided image editing with
region-based attention,
PR(139), 2023, pp. 109458.
Elsevier DOI
2304
Generative adversarial networks, Text-guided image editing,
Spatial disentanglement
BibRef
Nian, F.D.[Fu-Dong],
Li, T.[Teng],
Wang, Y.[Yan],
Wu, X.Y.[Xin-Yu],
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Learning explicit video attributes from mid-level representation for
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CVIU(163), No. 1, 2017, pp. 126-138.
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1712
Mid-level video representation
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Ye, S.,
Han, J.,
Liu, N.,
Attentive Linear Transformation for Image Captioning,
IP(27), No. 11, November 2018, pp. 5514-5524.
IEEE DOI
1809
feature extraction, image classification,
learning (artificial intelligence), matrix algebra, probability,
LSTM
BibRef
Xian, Y.,
Tian, Y.,
Self-Guiding Multimodal LSTM: When We Do Not Have a Perfect Training
Dataset for Image Captioning,
IP(28), No. 11, November 2019, pp. 5241-5252.
IEEE DOI
1909
Task analysis, Visualization, Training, Semantics, Flickr, Urban areas,
Training data, Image captioning, self-guiding, real-world dataset,
recurrent neural network
BibRef
Peng, Y.Q.[Yu-Qing],
Liu, X.[Xuan],
Wang, W.H.[Wei-Hua],
Zhao, X.S.[Xiao-Song],
Wei, M.[Ming],
Image caption model of double LSTM with scene factors,
IVC(86), 2019, pp. 38-44.
Elsevier DOI
1906
Image caption, Deep neural network, Scene recognition, Semantic information
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Wu, L.,
Xu, M.,
Wang, J.,
Perry, S.,
Recall What You See Continually Using GridLSTM in Image Captioning,
MultMed(22), No. 3, March 2020, pp. 808-818.
IEEE DOI
2003
Visualization, Decoding, Task analysis, Neural networks, Training,
Computational modeling, Logic gates, Image captioning,
GridLSTM, recurrent neural network
BibRef
Deng, Z.R.[Zhen-Rong],
Jiang, Z.Q.[Zhou-Qin],
Lan, R.[Rushi],
Huang, W.M.[Wen-Ming],
Luo, X.N.[Xiao-Nan],
Image captioning using DenseNet network and adaptive attention,
SP:IC(85), 2020, pp. 115836.
Elsevier DOI
2005
Image captioning, DenseNet, LSTM, Adaptive attention mechanism
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Ji, J.,
Xu, C.,
Zhang, X.,
Wang, B.,
Song, X.,
Spatio-Temporal Memory Attention for Image Captioning,
IP(29), 2020, pp. 7615-7628.
IEEE DOI
2007
Image captioning, spatio-temporal relationship,
attention transmission, memory attention, LSTM
BibRef
Che, W.B.[Wen-Bin],
Fan, X.P.[Xiao-Peng],
Xiong, R.Q.[Rui-Qin],
Zhao, D.B.[De-Bin],
Visual Relationship Embedding Network for Image Paragraph Generation,
MultMed(22), No. 9, September 2020, pp. 2307-2320.
IEEE DOI
2008
Visualization, Semantics, Task analysis, Proposals, Automobiles,
Buildings, Paragraph generation, image caption, LSTM
BibRef
Zhang, J.[Jing],
Li, K.K.[Kang-Kang],
Wang, Z.[Zhe],
Parallel-fusion LSTM with synchronous semantic and visual information
for image captioning,
JVCIR(75), 2021, pp. 103044.
Elsevier DOI
2103
Image captioning, Parallel-fusion LSTM, Attention mechanism, Guiding LSTM
BibRef
He, S.[Shan],
Lu, Y.Y.[Yuan-Yao],
Chen, S.N.[Sheng-Nan],
Image Captioning Algorithm Based on Multi-Branch CNN and Bi-LSTM,
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Yuan, J.[Jin],
Zhu, S.[Shuai],
Huang, S.Y.[Shu-Yin],
Zhang, H.W.[Han-Wang],
Xiao, Y.Q.[Yao-Qiang],
Li, Z.Y.[Zhi-Yong],
Wang, M.[Meng],
Discriminative Style Learning for Cross-Domain Image Captioning,
IP(31), 2022, pp. 1723-1736.
IEEE DOI
2202
Decoding, Visualization, Syntactics, Semantics, Training, Logic gates,
Birds, Cross-domain, image captioning, style, instruction-based LSTM
BibRef
Zhou, Y.[Yuanen],
Zhang, Y.[Yong],
Hu, Z.Z.[Zhen-Zhen],
Wang, M.[Meng],
Semi-Autoregressive Transformer for Image Captioning,
CLVL21(3132-3136)
IEEE DOI
2112
Training, Degradation, Codes, Benchmark testing, Transformers
BibRef
Lv, G.[Gang],
Sun, Y.N.[Yi-Ning],
Nian, F.[Fudong],
Zhu, M.F.[Mao-Fei],
Tang, W.L.[Wen-Liang],
Hu, Z.Z.[Zhen-Zhen],
COME: Clip-OCR and Master ObjEct for text image captioning,
IVC(136), 2023, pp. 104751.
Elsevier DOI
2308
Image captioning, Graph convolution network, OCR, LSTM
BibRef
Tan, Y.H.[Ying Hua],
Chan, C.S.[Chee Seng],
phi-LSTM: A Phrase-Based Hierarchical LSTM Model for Image Captioning,
ACCV16(V: 101-117).
Springer DOI
1704
BibRef
Wang, M.[Minsi],
Song, L.[Li],
Yang, X.K.[Xiao-Kang],
Luo, C.F.[Chuan-Fei],
A parallel-fusion RNN-LSTM architecture for image caption generation,
ICIP16(4448-4452)
IEEE DOI
1610
Computational modeling
deep convolutional networks and recurrent neural networks.
BibRef
Chapter on Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants continues in
Multi-Modal, Cross-Modal Captioning, Image Captioning .