MFAS: Multimodal Fusion Architecture Search

We tackle the problem of finding good architectures for multimodal classification problems. We propose a novel and generic search space that spans a large number of possible fusion architectures. In order to find an optimal architecture for a given dataset in the proposed search space, we leverage an efficient sequential model-based exploration approach that is tailored for the problem. We demonstrate the value of posing multimodal fusion as a neural architecture search problem by extensive experimentation on a toy dataset and two other real multimodal datasets. We discover fusion architectures that exhibit state-of-the-art performance for problems with different domain and dataset size, including the \ntu~dataset, the largest multimodal action recognition dataset available.

[1]  Chao Li,et al.  Co-occurrence Feature Learning from Skeleton Data for Action Recognition and Detection with Hierarchical Aggregation , 2018, IJCAI.

[2]  Jeff A. Bilmes,et al.  Deep Canonical Correlation Analysis , 2013, ICML.

[3]  Quoc V. Le,et al.  Efficient Neural Architecture Search via Parameter Sharing , 2018, ICML.

[4]  Kevin Leyton-Brown,et al.  Sequential Model-Based Optimization for General Algorithm Configuration , 2011, LION.

[5]  Stéphane Pateux,et al.  Efficient Progressive Neural Architecture Search , 2018, BMVC.

[6]  David Picard,et al.  2D/3D Pose Estimation and Action Recognition Using Multitask Deep Learning , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[7]  Christian Wolf,et al.  Sequential Deep Learning for Human Action Recognition , 2011, HBU.

[8]  Pavlo Molchanov,et al.  Multilayer and Multimodal Fusion of Deep Neural Networks for Video Classification , 2016, ACM Multimedia.

[9]  Geoffrey E. Hinton,et al.  Adaptive Mixtures of Local Experts , 1991, Neural Computation.

[10]  Mohan S. Kankanhalli,et al.  Multimodal fusion for multimedia analysis: a survey , 2010, Multimedia Systems.

[11]  Edward Y. Chang,et al.  Optimal multimodal fusion for multimedia data analysis , 2004, MULTIMEDIA '04.

[12]  Xiaoli Zhou,et al.  Feature fusion of side face and gait for video-based human identification , 2008, Pattern Recognit..

[13]  Gang Wang,et al.  Deep Multimodal Feature Analysis for Action Recognition in RGB+D Videos , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Martin Engilberge,et al.  Finding Beans in Burgers: Deep Semantic-Visual Embedding with Localization , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[15]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[16]  Frédéric Jurie,et al.  CentralNet: a Multilayer Approach for Multimodal Fusion , 2018, ECCV Workshops.

[17]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[18]  Matthieu Cord,et al.  MUTAN: Multimodal Tucker Fusion for Visual Question Answering , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[19]  Andrew Zisserman,et al.  Two-Stream Convolutional Networks for Action Recognition in Videos , 2014, NIPS.

[20]  Jian-Huang Lai,et al.  Deep Bilinear Learning for RGB-D Action Recognition , 2018, ECCV.

[21]  Shuang Wu,et al.  Multimodal feature fusion for robust event detection in web videos , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[22]  Xiao Liu,et al.  Multimodal Keyless Attention Fusion for Video Classification , 2018, AAAI.

[23]  Dong Liu,et al.  Robust late fusion with rank minimization , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[24]  Shih-Fu Chang,et al.  Exploiting Feature and Class Relationships in Video Categorization with Regularized Deep Neural Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[25]  Tara N. Sainath,et al.  FUNDAMENTAL TECHNOLOGIES IN MODERN SPEECH RECOGNITION Digital Object Identifier 10.1109/MSP.2012.2205597 , 2012 .

[26]  Fabio A. González,et al.  Gated Multimodal Units for Information Fusion , 2017, ICLR.

[27]  Quoc V. Le,et al.  Neural Architecture Search with Reinforcement Learning , 2016, ICLR.

[28]  Christian Wolf,et al.  Multi-scale Deep Learning for Gesture Detection and Localization , 2014, ECCV Workshops.

[29]  Lei Huang,et al.  Learning Joint Multimodal Representation Based on Multi-fusion Deep Neural Networks , 2017, ICONIP.

[30]  Mohamed R. Amer,et al.  Deep Multimodal Fusion: A Hybrid Approach , 2017, International Journal of Computer Vision.

[31]  Bernard Ghanem,et al.  On the relationship between visual attributes and convolutional networks , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[32]  Li Fei-Fei,et al.  Progressive Neural Architecture Search , 2017, ECCV.

[33]  Juhan Nam,et al.  Multimodal Deep Learning , 2011, ICML.

[34]  Jia Deng,et al.  Stacked Hourglass Networks for Human Pose Estimation , 2016, ECCV.

[35]  John R. Hershey,et al.  Attention-Based Multimodal Fusion for Video Description , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[36]  Theodore Lim,et al.  SMASH: One-Shot Model Architecture Search through HyperNetworks , 2017, ICLR.

[37]  Christian Wolf,et al.  Modout: Learning to Fuse Modalities via Stochastic Regularization , 2016 .

[38]  Vijay Vasudevan,et al.  Learning Transferable Architectures for Scalable Image Recognition , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[39]  Yoshua Bengio,et al.  Maxout Networks , 2013, ICML.

[40]  Lawrence D. Jackel,et al.  Handwritten Digit Recognition with a Back-Propagation Network , 1989, NIPS.

[41]  Karol J. Piczak.  ESC: Dataset for Environmental Sound Classification , 2015, ACM Multimedia.

[42]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[43]  George Kurian,et al.  Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation , 2016, ArXiv.

[44]  Gang Wang,et al.  NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[45]  Christian Wolf,et al.  ModDrop: Adaptive Multi-Modal Gesture Recognition , 2014, IEEE Trans. Pattern Anal. Mach. Intell..

[46]  Michael I. Jordan,et al.  Multiple kernel learning, conic duality, and the SMO algorithm , 2004, ICML.

[47]  Christian Wolf,et al.  Glimpse Clouds: Human Activity Recognition from Unstructured Feature Points , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[48]  Wenjun Zeng,et al.  An End-to-End Spatio-Temporal Attention Model for Human Action Recognition from Skeleton Data , 2016, AAAI.