AWDF: An Adaptive Weighted Deep Fusion Architecture for Multi-modality Learning
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Xiaolong Wang | Steven Eliuk | Qinghan Xue | Abhishek Kolagunda | Abhishek Kolagunda | Qinghan Xue | S. Eliuk | Xiaolong Wang
[1] Akshita Gupta,et al. Acoustic Features Fusion using Attentive Multi-channel Deep Architecture , 2018, 5th International Workshop on Speech Processing in Everyday Environments (CHiME 2018).
[2] Frédéric Jurie,et al. MFAS: Multimodal Fusion Architecture Search , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[3] Jiwen Lu,et al. MMSS: Multi-modal Sharable and Specific Feature Learning for RGB-D Object Recognition , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[4] Christopher D. Manning,et al. Effective Approaches to Attention-based Neural Machine Translation , 2015, EMNLP.
[5] Heather N. Watson,et al. Use of electronic medical records (EMR) for oncology outcomes research: assessing the comparability of EMR information to patient registry and health claims data , 2011, Clinical epidemiology.
[6] Shuang Wu,et al. Multimodal feature fusion for robust event detection in web videos , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[7] Tieniu Tan,et al. DF2Net: Discriminative Feature Learning and Fusion Network for RGB-D Indoor Scene Classification , 2018, AAAI.
[8] Wolfram Burgard,et al. Multimodal deep learning for robust RGB-D object recognition , 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
[9] Xi Wang,et al. Modeling Spatial-Temporal Clues in a Hybrid Deep Learning Framework for Video Classification , 2015, ACM Multimedia.
[10] Tara N. Sainath,et al. Deep Neural Networks for Acoustic Modeling in Speech Recognition , 2012 .
[11] Sukhendu Das,et al. A Survey of Decision Fusion and Feature Fusion Strategies for Pattern Classification , 2010, IETE Technical Review.
[12] B. Dean,et al. Review: Use of Electronic Medical Records for Health Outcomes Research , 2009, Medical care research and review : MCRR.
[13] Nitish Srivastava,et al. Multimodal learning with deep Boltzmann machines , 2012, J. Mach. Learn. Res..
[14] Han Wang,et al. AcFR: Active Face Recognition Using Convolutional Neural Networks , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[15] Olga R. P. Bellon,et al. AUMPNet: Simultaneous Action Units Detection and Intensity Estimation on Multipose Facial Images Using a Single Convolutional Neural Network , 2017, 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017).
[16] Christopher Joseph Pal,et al. EmoNets: Multimodal deep learning approaches for emotion recognition in video , 2015, Journal on Multimodal User Interfaces.
[17] Huy Phan,et al. Improved Audio Scene Classification Based on Label-Tree Embeddings and Convolutional Neural Networks , 2017, IEEE/ACM Transactions on Audio, Speech, and Language Processing.
[18] S. Squartini,et al. DCASE 2016 Acoustic Scene Classification Using Convolutional Neural Networks , 2016, DCASE.
[19] Christian Wolf,et al. Multi-scale Deep Learning for Gesture Detection and Localization , 2014, ECCV Workshops.
[20] Jeffrey Pennington,et al. GloVe: Global Vectors for Word Representation , 2014, EMNLP.
[21] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[22] Nicu Sebe,et al. Learning Deep Representations of Appearance and Motion for Anomalous Event Detection , 2015, BMVC.
[23] Andrew Zisserman,et al. Convolutional Two-Stream Network Fusion for Video Action Recognition , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).