7,266 research outputs found
Lattice Recurrent Unit: Improving Convergence and Statistical Efficiency for Sequence Modeling
Recurrent neural networks have shown remarkable success in modeling
sequences. However low resource situations still adversely affect the
generalizability of these models. We introduce a new family of models, called
Lattice Recurrent Units (LRU), to address the challenge of learning deep
multi-layer recurrent models with limited resources. LRU models achieve this
goal by creating distinct (but coupled) flow of information inside the units: a
first flow along time dimension and a second flow along depth dimension. It
also offers a symmetry in how information can flow horizontally and vertically.
We analyze the effects of decoupling three different components of our LRU
model: Reset Gate, Update Gate and Projected State. We evaluate this family on
new LRU models on computational convergence rates and statistical efficiency.
Our experiments are performed on four publicly-available datasets, comparing
with Grid-LSTM and Recurrent Highway networks. Our results show that LRU has
better empirical computational convergence rates and statistical efficiency
values, along with learning more accurate language models.Comment: 8 pages, 7 figure
Deep Architectures and Ensembles for Semantic Video Classification
This work addresses the problem of accurate semantic labelling of short
videos. To this end, a multitude of different deep nets, ranging from
traditional recurrent neural networks (LSTM, GRU), temporal agnostic networks
(FV,VLAD,BoW), fully connected neural networks mid-stage AV fusion and others.
Additionally, we also propose a residual architecture-based DNN for video
classification, with state-of-the art classification performance at
significantly reduced complexity. Furthermore, we propose four new approaches
to diversity-driven multi-net ensembling, one based on fast correlation measure
and three incorporating a DNN-based combiner. We show that significant
performance gains can be achieved by ensembling diverse nets and we investigate
factors contributing to high diversity. Based on the extensive YouTube8M
dataset, we provide an in-depth evaluation and analysis of their behaviour. We
show that the performance of the ensemble is state-of-the-art achieving the
highest accuracy on the YouTube-8M Kaggle test data. The performance of the
ensemble of classifiers was also evaluated on the HMDB51 and UCF101 datasets,
and show that the resulting method achieves comparable accuracy with
state-of-the-art methods using similar input features
Two-Stream RNN/CNN for Action Recognition in 3D Videos
The recognition of actions from video sequences has many applications in
health monitoring, assisted living, surveillance, and smart homes. Despite
advances in sensing, in particular related to 3D video, the methodologies to
process the data are still subject to research. We demonstrate superior results
by a system which combines recurrent neural networks with convolutional neural
networks in a voting approach. The gated-recurrent-unit-based neural networks
are particularly well-suited to distinguish actions based on long-term
information from optical tracking data; the 3D-CNNs focus more on detailed,
recent information from video data. The resulting features are merged in an SVM
which then classifies the movement. In this architecture, our method improves
recognition rates of state-of-the-art methods by 14% on standard data sets.Comment: Published in 2017 IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS
- …