456 research outputs found
Continuous Action Recognition Based on Sequence Alignment
Continuous action recognition is more challenging than isolated recognition
because classification and segmentation must be simultaneously carried out. We
build on the well known dynamic time warping (DTW) framework and devise a novel
visual alignment technique, namely dynamic frame warping (DFW), which performs
isolated recognition based on per-frame representation of videos, and on
aligning a test sequence with a model sequence. Moreover, we propose two
extensions which enable to perform recognition concomitant with segmentation,
namely one-pass DFW and two-pass DFW. These two methods have their roots in the
domain of continuous recognition of speech and, to the best of our knowledge,
their extension to continuous visual action recognition has been overlooked. We
test and illustrate the proposed techniques with a recently released dataset
(RAVEL) and with two public-domain datasets widely used in action recognition
(Hollywood-1 and Hollywood-2). We also compare the performances of the proposed
isolated and continuous recognition algorithms with several recently published
methods
Light Gated Recurrent Units for Speech Recognition
A field that has directly benefited from the recent advances in deep learning
is Automatic Speech Recognition (ASR). Despite the great achievements of the
past decades, however, a natural and robust human-machine speech interaction
still appears to be out of reach, especially in challenging environments
characterized by significant noise and reverberation. To improve robustness,
modern speech recognizers often employ acoustic models based on Recurrent
Neural Networks (RNNs), that are naturally able to exploit large time contexts
and long-term speech modulations. It is thus of great interest to continue the
study of proper techniques for improving the effectiveness of RNNs in
processing speech signals.
In this paper, we revise one of the most popular RNN models, namely Gated
Recurrent Units (GRUs), and propose a simplified architecture that turned out
to be very effective for ASR. The contribution of this work is two-fold: First,
we analyze the role played by the reset gate, showing that a significant
redundancy with the update gate occurs. As a result, we propose to remove the
former from the GRU design, leading to a more efficient and compact single-gate
model. Second, we propose to replace hyperbolic tangent with ReLU activations.
This variation couples well with batch normalization and could help the model
learn long-term dependencies without numerical issues.
Results show that the proposed architecture, called Light GRU (Li-GRU), not
only reduces the per-epoch training time by more than 30% over a standard GRU,
but also consistently improves the recognition accuracy across different tasks,
input features, noisy conditions, as well as across different ASR paradigms,
ranging from standard DNN-HMM speech recognizers to end-to-end CTC models.Comment: Copyright 2018 IEE
Show, Attend and Read: A Simple and Strong Baseline for Irregular Text Recognition
Recognizing irregular text in natural scene images is challenging due to the
large variance in text appearance, such as curvature, orientation and
distortion. Most existing approaches rely heavily on sophisticated model
designs and/or extra fine-grained annotations, which, to some extent, increase
the difficulty in algorithm implementation and data collection. In this work,
we propose an easy-to-implement strong baseline for irregular scene text
recognition, using off-the-shelf neural network components and only word-level
annotations. It is composed of a -layer ResNet, an LSTM-based
encoder-decoder framework and a 2-dimensional attention module. Despite its
simplicity, the proposed method is robust and achieves state-of-the-art
performance on both regular and irregular scene text recognition benchmarks.
Code is available at: https://tinyurl.com/ShowAttendReadComment: Accepted to Proc. AAAI Conference on Artificial Intelligence 201
An Effective Combination of Different Order N-grams
In this paper an approach is proposed to combine different order N-grams based on the discriminative estimation criterion, on which the parameters of n-gram can be optimized. To raise the power of modeling language information, we propose several schemes to combine conventional different order n-gram language model. We employ Newton Gradient method to estimate the assumption probabilities and then test the optimally selected language model. We conduct experiments on the platform of conversion from Chinese pinyin to Chinese character. The experimental results show that the memory capacity of language model can be remarkably lowered with hide loss of accuracy. 1
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