Open AccessProceedings Article
Multimodal Deep Learning
Jiquan Ngiam,Aditya Khosla,Mingyu Kim,Juhan Nam,Honglak Lee,Andrew Y. Ng +5 more
- pp 689-696
Reads0
Chats0
TLDR
This work presents a series of tasks for multimodal learning and shows how to train deep networks that learn features to address these tasks, and demonstrates cross modality feature learning, where better features for one modality can be learned if multiple modalities are present at feature learning time.Abstract:
Deep networks have been successfully applied to unsupervised feature learning for single modalities (e.g., text, images or audio). In this work, we propose a novel application of deep networks to learn features over multiple modalities. We present a series of tasks for multimodal learning and show how to train deep networks that learn features to address these tasks. In particular, we demonstrate cross modality feature learning, where better features for one modality (e.g., video) can be learned if multiple modalities (e.g., audio and video) are present at feature learning time. Furthermore, we show how to learn a shared representation between modalities and evaluate it on a unique task, where the classifier is trained with audio-only data but tested with video-only data and vice-versa. Our models are validated on the CUAVE and AVLetters datasets on audio-visual speech classification, demonstrating best published visual speech classification on AVLetters and effective shared representation learning.read more
Citations
More filters
Proceedings ArticleDOI
Using Clinical Notes with Time Series Data for ICU Management
TL;DR: This work shows that adding clinical notes as another modality improves the performance of the model for three benchmark tasks: in-hospital mortality prediction, modeling decompensation, and length of stay forecasting that play an important role in ICU management.
Journal ArticleDOI
Multi-modal semantic autoencoder for cross-modal retrieval
TL;DR: A two-stage learning method to learn multi- modal mappings that project multi-modal data to low dimensional embeddings that preserve both feature and semantic information that outperforms state-of-the-art cross-modAL retrieval methods.
Proceedings Article
Learning Visually-Grounded Semantics from Contrastive Adversarial Samples
TL;DR: This work augments the MS-COCO image captioning datasets with textual contrastive adversarial samples, and enforce the model to ground learned embeddings to concrete concepts within the image, in addition to defending known-type adversarial attacks.
Journal ArticleDOI
Uniform and Variational Deep Learning for RGB-D Object Recognition and Person Re-Identification
TL;DR: A uniform and variational deep learning (UVDL) method for RGB-D object recognition and person re-identification that recognizes visual objects and persons withRGB-D images to exploit more reliable information such as geometric and anthropometric information that are robust to different viewpoints.
Proceedings ArticleDOI
Aligning Audiovisual Features for Audiovisual Speech Recognition
Fei Tao,Carlos Busso +1 more
TL;DR: This paper addresses the fusion of audiovisual features with an alignment neural network (AliNN), relying on recurrent Neural Network (RNN) with attention model, and proposes a front-end model that can automatically learn the alignment from the data.
References
More filters
Proceedings ArticleDOI
Histograms of oriented gradients for human detection
Navneet Dalal,Bill Triggs +1 more
TL;DR: It is shown experimentally that grids of histograms of oriented gradient (HOG) descriptors significantly outperform existing feature sets for human detection, and the influence of each stage of the computation on performance is studied.
Journal ArticleDOI
Reducing the Dimensionality of Data with Neural Networks
TL;DR: In this article, an effective way of initializing the weights that allows deep autoencoder networks to learn low-dimensional codes that work much better than principal components analysis as a tool to reduce the dimensionality of data is described.
Journal ArticleDOI
A fast learning algorithm for deep belief nets
TL;DR: A fast, greedy algorithm is derived that can learn deep, directed belief networks one layer at a time, provided the top two layers form an undirected associative memory.
Proceedings ArticleDOI
Extracting and composing robust features with denoising autoencoders
TL;DR: This work introduces and motivate a new training principle for unsupervised learning of a representation based on the idea of making the learned representations robust to partial corruption of the input pattern.
Journal ArticleDOI
Hearing lips and seeing voices
Harry McGurk,John Macdonald +1 more
TL;DR: The study reported here demonstrates a previously unrecognised influence of vision upon speech perception, on being shown a film of a young woman's talking head in which repeated utterances of the syllable [ba] had been dubbed on to lip movements for [ga].