18,263 research outputs found
What-and-Where to Match: Deep Spatially Multiplicative Integration Networks for Person Re-identification
Matching pedestrians across disjoint camera views, known as person
re-identification (re-id), is a challenging problem that is of importance to
visual recognition and surveillance. Most existing methods exploit local
regions within spatial manipulation to perform matching in local
correspondence. However, they essentially extract \emph{fixed} representations
from pre-divided regions for each image and perform matching based on the
extracted representation subsequently. For models in this pipeline, local finer
patterns that are crucial to distinguish positive pairs from negative ones
cannot be captured, and thus making them underperformed. In this paper, we
propose a novel deep multiplicative integration gating function, which answers
the question of \emph{what-and-where to match} for effective person re-id. To
address \emph{what} to match, our deep network emphasizes common local patterns
by learning joint representations in a multiplicative way. The network
comprises two Convolutional Neural Networks (CNNs) to extract convolutional
activations, and generates relevant descriptors for pedestrian matching. This
thus, leads to flexible representations for pair-wise images. To address
\emph{where} to match, we combat the spatial misalignment by performing
spatially recurrent pooling via a four-directional recurrent neural network to
impose spatial dependency over all positions with respect to the entire image.
The proposed network is designed to be end-to-end trainable to characterize
local pairwise feature interactions in a spatially aligned manner. To
demonstrate the superiority of our method, extensive experiments are conducted
over three benchmark data sets: VIPeR, CUHK03 and Market-1501.Comment: Published at Pattern Recognition, Elsevie
Two Stream LSTM: A Deep Fusion Framework for Human Action Recognition
In this paper we address the problem of human action recognition from video
sequences. Inspired by the exemplary results obtained via automatic feature
learning and deep learning approaches in computer vision, we focus our
attention towards learning salient spatial features via a convolutional neural
network (CNN) and then map their temporal relationship with the aid of
Long-Short-Term-Memory (LSTM) networks. Our contribution in this paper is a
deep fusion framework that more effectively exploits spatial features from CNNs
with temporal features from LSTM models. We also extensively evaluate their
strengths and weaknesses. We find that by combining both the sets of features,
the fully connected features effectively act as an attention mechanism to
direct the LSTM to interesting parts of the convolutional feature sequence. The
significance of our fusion method is its simplicity and effectiveness compared
to other state-of-the-art methods. The evaluation results demonstrate that this
hierarchical multi stream fusion method has higher performance compared to
single stream mapping methods allowing it to achieve high accuracy
outperforming current state-of-the-art methods in three widely used databases:
UCF11, UCFSports, jHMDB.Comment: Published as a conference paper at WACV 201
Time-Contrastive Learning Based Deep Bottleneck Features for Text-Dependent Speaker Verification
There are a number of studies about extraction of bottleneck (BN) features
from deep neural networks (DNNs)trained to discriminate speakers, pass-phrases
and triphone states for improving the performance of text-dependent speaker
verification (TD-SV). However, a moderate success has been achieved. A recent
study [1] presented a time contrastive learning (TCL) concept to explore the
non-stationarity of brain signals for classification of brain states. Speech
signals have similar non-stationarity property, and TCL further has the
advantage of having no need for labeled data. We therefore present a TCL based
BN feature extraction method. The method uniformly partitions each speech
utterance in a training dataset into a predefined number of multi-frame
segments. Each segment in an utterance corresponds to one class, and class
labels are shared across utterances. DNNs are then trained to discriminate all
speech frames among the classes to exploit the temporal structure of speech. In
addition, we propose a segment-based unsupervised clustering algorithm to
re-assign class labels to the segments. TD-SV experiments were conducted on the
RedDots challenge database. The TCL-DNNs were trained using speech data of
fixed pass-phrases that were excluded from the TD-SV evaluation set, so the
learned features can be considered phrase-independent. We compare the
performance of the proposed TCL bottleneck (BN) feature with those of
short-time cepstral features and BN features extracted from DNNs discriminating
speakers, pass-phrases, speaker+pass-phrase, as well as monophones whose labels
and boundaries are generated by three different automatic speech recognition
(ASR) systems. Experimental results show that the proposed TCL-BN outperforms
cepstral features and speaker+pass-phrase discriminant BN features, and its
performance is on par with those of ASR derived BN features. Moreover,....Comment: Copyright (c) 2019 IEEE. Personal use of this material is permitted.
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Dynamic feature selection for clustering high dimensional data streams
open access articleChange in a data stream can occur at the concept level and at the feature level. Change at the feature level can occur if new, additional features appear in the stream or if the importance and relevance of a feature changes as the stream progresses. This type of change has not received as much attention as concept-level change. Furthermore, a lot of the methods proposed for clustering streams (density-based, graph-based, and grid-based) rely on some form of distance as a similarity metric and this is problematic in high-dimensional data where the curse of dimensionality renders distance measurements and any concept of “density” difficult. To address these two challenges we propose combining them and framing the problem as a feature selection problem, specifically a dynamic feature selection problem. We propose a dynamic feature mask for clustering high dimensional data streams. Redundant features are masked and clustering is performed along unmasked, relevant features. If a feature's perceived importance changes, the mask is updated accordingly; previously unimportant features are unmasked and features which lose relevance become masked. The proposed method is algorithm-independent and can be used with any of the existing density-based clustering algorithms which typically do not have a mechanism for dealing with feature drift and struggle with high-dimensional data. We evaluate the proposed method on four density-based clustering algorithms across four high-dimensional streams; two text streams and two image streams. In each case, the proposed dynamic feature mask improves clustering performance and reduces the processing time required by the underlying algorithm. Furthermore, change at the feature level can be observed and tracked
Efficient Action Detection in Untrimmed Videos via Multi-Task Learning
This paper studies the joint learning of action recognition and temporal
localization in long, untrimmed videos. We employ a multi-task learning
framework that performs the three highly related steps of action proposal,
action recognition, and action localization refinement in parallel instead of
the standard sequential pipeline that performs the steps in order. We develop a
novel temporal actionness regression module that estimates what proportion of a
clip contains action. We use it for temporal localization but it could have
other applications like video retrieval, surveillance, summarization, etc. We
also introduce random shear augmentation during training to simulate viewpoint
change. We evaluate our framework on three popular video benchmarks. Results
demonstrate that our joint model is efficient in terms of storage and
computation in that we do not need to compute and cache dense trajectory
features, and that it is several times faster than its sequential ConvNets
counterpart. Yet, despite being more efficient, it outperforms state-of-the-art
methods with respect to accuracy.Comment: WACV 2017 camera ready, minor updates about test time efficienc
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