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Pengjie Tang
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2020 – today
- 2024
- [j17]Yun Yi, Jin Zhou, Hanli Wang, Pengjie Tang, Min Wang:
Emotion recognition in user-generated videos with long-range correlation-aware network. IET Image Process. 18(12): 3288-3301 (2024) - [j16]Pengjie Tang, Hong Rao, Ai Zhang, Yunlan Tan:
Video emotional description with fact reinforcement and emotion awaking. J. Ambient Intell. Humaniz. Comput. 15(6): 2839-2852 (2024) - 2023
- [j15]Dengli Bu, Junyi Deng, Pengjie Tang, Shuhong Yang:
Reversible Circuit Synthesis Method Using Sub-graphs of Shared Functional Decision Diagrams. Comput. J. 66(10): 2574-2592 (2023) - [j14]Pengjie Tang, Yunlan Tan, Jiewu Xia:
Deep sequential collaborative cognition of vision and language based model for video description. Multim. Tools Appl. 82(23): 36207-36230 (2023) - 2022
- [j13]Dengli Bu, Junjie Yan, Pengjie Tang, Haohao Yuan:
Synthesis of Reversible Circuits with Reduced Nearest-Neighbor Cost Using Kronecker Functional Decision Diagrams. J. Electron. Test. 38(1): 39-62 (2022) - [j12]Pengjie Tang, Yunlan Tan, Wenlang Luo:
Visual and language semantic hybrid enhancement and complementary for video description. Neural Comput. Appl. 34(8): 5959-5977 (2022) - [j11]Hanli Wang, Pengjie Tang, Qinyu Li, Meng Cheng:
Emotion Expression With Fact Transfer for Video Description. IEEE Trans. Multim. 24: 715-727 (2022) - [c6]Qinyu Zhang, Pengjie Tang, Hanli Wang, Jinjing Gu:
Multi-concept Mining for Video Captioning Based on Multiple Tasks. ISCAS 2022: 2993-2997 - [c5]Quanhui Cao, Pengjie Tang, Hanli Wang:
Spatio-temporal Super-resolution Network: Enhance Visual Representations for Video Captioning. ISCAS 2022: 3125-3129 - 2021
- [j10]Longyu Yang, Hanli Wang, Pengjie Tang, Qinyu Li:
CaptionNet: A Tailor-made Recurrent Neural Network for Generating Image Descriptions. IEEE Trans. Multim. 23: 835-845 (2021) - 2020
- [j9]Yu Long, Pengjie Tang, Zhihua Wei, Jinjing Gu, Hanli Wang:
RepeatPadding: Balancing words and sentence length for language comprehension in visual question answering. Inf. Sci. 529: 166-178 (2020) - [j8]Pengjie Tang, Yunlan Tan, Jinzhong Li, Bin Tan:
Translating video into language by enhancing visual and language representations. J. Vis. Commun. Image Represent. 72: 102875 (2020) - [j7]Pengjie Tang, Jiewu Xia, Yunlan Tan, Bin Tan:
Double-channel language feature mining based model for video description. Multim. Tools Appl. 79(43-44): 33193-33213 (2020)
2010 – 2019
- 2019
- [j6]Pengjie Tang, Hanli Wang, Qinyu Li:
Rich Visual and Language Representation with Complementary Semantics for Video Captioning. ACM Trans. Multim. Comput. Commun. Appl. 15(2): 31:1-31:23 (2019) - 2018
- [j5]Pengjie Tang, Hanli Wang, Sam Kwong:
Deep sequential fusion LSTM network for image description. Neurocomputing 312: 154-164 (2018) - [j4]Fang Fang, Hanli Wang, Yihao Chen, Pengjie Tang:
Looking deeper and transferring attention for image captioning. Multim. Tools Appl. 77(23): 31159-31175 (2018) - [c4]Fang Fang, Hanli Wang, Pengjie Tang:
Image Captioning with Word Level Attention. ICIP 2018: 1278-1282 - [c3]Fang Fang, Qinyu Li, Hanli Wang, Pengjie Tang:
Refining Attention: A Sequential Attention Model for Image Captioning. ICME 2018: 1-6 - 2017
- [j3]Pengjie Tang, Hanli Wang:
Richer feature for image classification with super and sub kernels based on deep convolutional neural network. Comput. Electr. Eng. 62: 499-510 (2017) - [j2]Pengjie Tang, Hanli Wang, Sam Kwong:
G-MS2F: GoogLeNet based multi-stage feature fusion of deep CNN for scene recognition. Neurocomputing 225: 188-197 (2017) - [j1]Yunlan Tan, Pengjie Tang, Yimin Zhou, Wenlang Luo, Yongping Kang, Guangyao Li:
Photograph aesthetical evaluation and classification with deep convolutional neural networks. Neurocomputing 228: 165-175 (2017) - [c2]Kaisheng Xu, Hanli Wang, Pengjie Tang:
Image captioning with deep LSTM based on sequential residual. ICME 2017: 361-366 - [c1]Pengjie Tang, Hanli Wang, Hanzhang Wang, Kaisheng Xu:
Richer Semantic Visual and Language Representation for Video Captioning. ACM Multimedia 2017: 1871-1876
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last updated on 2024-12-10 20:49 CET by the dblp team
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