102,679 research outputs found
ODN: Opening the Deep Network for Open-set Action Recognition
In recent years, the performance of action recognition has been significantly
improved with the help of deep neural networks. Most of the existing action
recognition works hold the \textit{closed-set} assumption that all action
categories are known beforehand while deep networks can be well trained for
these categories. However, action recognition in the real world is essentially
an \textit{open-set} problem, namely, it is impossible to know all action
categories beforehand and consequently infeasible to prepare sufficient
training samples for those emerging categories. In this case, applying
closed-set recognition methods will definitely lead to unseen-category errors.
To address this challenge, we propose the Open Deep Network (ODN) for the
open-set action recognition task. Technologically, ODN detects new categories
by applying a multi-class triplet thresholding method, and then dynamically
reconstructs the classification layer and "opens" the deep network by adding
predictors for new categories continually. In order to transfer the learned
knowledge to the new category, two novel methods, Emphasis Initialization and
Allometry Training, are adopted to initialize and incrementally train the new
predictor so that only few samples are needed to fine-tune the model. Extensive
experiments show that ODN can effectively detect and recognize new categories
with little human intervention, thus applicable to the open-set action
recognition tasks in the real world. Moreover, ODN can even achieve comparable
performance to some closed-set methods.Comment: 6 pages, 3 figures, ICME 201
Co-occurrence Feature Learning for Skeleton based Action Recognition using Regularized Deep LSTM Networks
Skeleton based action recognition distinguishes human actions using the
trajectories of skeleton joints, which provide a very good representation for
describing actions. Considering that recurrent neural networks (RNNs) with Long
Short-Term Memory (LSTM) can learn feature representations and model long-term
temporal dependencies automatically, we propose an end-to-end fully connected
deep LSTM network for skeleton based action recognition. Inspired by the
observation that the co-occurrences of the joints intrinsically characterize
human actions, we take the skeleton as the input at each time slot and
introduce a novel regularization scheme to learn the co-occurrence features of
skeleton joints. To train the deep LSTM network effectively, we propose a new
dropout algorithm which simultaneously operates on the gates, cells, and output
responses of the LSTM neurons. Experimental results on three human action
recognition datasets consistently demonstrate the effectiveness of the proposed
model.Comment: AAAI 2016 conferenc
Interpretable 3D Human Action Analysis with Temporal Convolutional Networks
The discriminative power of modern deep learning models for 3D human action
recognition is growing ever so potent. In conjunction with the recent
resurgence of 3D human action representation with 3D skeletons, the quality and
the pace of recent progress have been significant. However, the inner workings
of state-of-the-art learning based methods in 3D human action recognition still
remain mostly black-box. In this work, we propose to use a new class of models
known as Temporal Convolutional Neural Networks (TCN) for 3D human action
recognition. Compared to popular LSTM-based Recurrent Neural Network models,
given interpretable input such as 3D skeletons, TCN provides us a way to
explicitly learn readily interpretable spatio-temporal representations for 3D
human action recognition. We provide our strategy in re-designing the TCN with
interpretability in mind and how such characteristics of the model is leveraged
to construct a powerful 3D activity recognition method. Through this work, we
wish to take a step towards a spatio-temporal model that is easier to
understand, explain and interpret. The resulting model, Res-TCN, achieves
state-of-the-art results on the largest 3D human action recognition dataset,
NTU-RGBD.Comment: 8 pages, 5 figures, BNMW CVPR 2017 Submissio
Deep Learning For Smile Recognition
Inspired by recent successes of deep learning in computer vision, we propose
a novel application of deep convolutional neural networks to facial expression
recognition, in particular smile recognition. A smile recognition test accuracy
of 99.45% is achieved for the Denver Intensity of Spontaneous Facial Action
(DISFA) database, significantly outperforming existing approaches based on
hand-crafted features with accuracies ranging from 65.55% to 79.67%. The
novelty of this approach includes a comprehensive model selection of the
architecture parameters, allowing to find an appropriate architecture for each
expression such as smile. This is feasible because all experiments were run on
a Tesla K40c GPU, allowing a speedup of factor 10 over traditional computations
on a CPU.Comment: Proceedings of the 12th Conference on Uncertainty Modelling in
Knowledge Engineering and Decision Making (FLINS 2016
DIY Human Action Data Set Generation
The recent successes in applying deep learning techniques to solve standard
computer vision problems has aspired researchers to propose new computer vision
problems in different domains. As previously established in the field, training
data itself plays a significant role in the machine learning process,
especially deep learning approaches which are data hungry. In order to solve
each new problem and get a decent performance, a large amount of data needs to
be captured which may in many cases pose logistical difficulties. Therefore,
the ability to generate de novo data or expand an existing data set, however
small, in order to satisfy data requirement of current networks may be
invaluable. Herein, we introduce a novel way to partition an action video clip
into action, subject and context. Each part is manipulated separately and
reassembled with our proposed video generation technique. Furthermore, our
novel human skeleton trajectory generation along with our proposed video
generation technique, enables us to generate unlimited action recognition
training data. These techniques enables us to generate video action clips from
an small set without costly and time-consuming data acquisition. Lastly, we
prove through extensive set of experiments on two small human action
recognition data sets, that this new data generation technique can improve the
performance of current action recognition neural nets
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