15,652 research outputs found
Object Detection in 20 Years: A Survey
Object detection, as of one the most fundamental and challenging problems in
computer vision, has received great attention in recent years. Its development
in the past two decades can be regarded as an epitome of computer vision
history. If we think of today's object detection as a technical aesthetics
under the power of deep learning, then turning back the clock 20 years we would
witness the wisdom of cold weapon era. This paper extensively reviews 400+
papers of object detection in the light of its technical evolution, spanning
over a quarter-century's time (from the 1990s to 2019). A number of topics have
been covered in this paper, including the milestone detectors in history,
detection datasets, metrics, fundamental building blocks of the detection
system, speed up techniques, and the recent state of the art detection methods.
This paper also reviews some important detection applications, such as
pedestrian detection, face detection, text detection, etc, and makes an in-deep
analysis of their challenges as well as technical improvements in recent years.Comment: This work has been submitted to the IEEE TPAMI for possible
publicatio
Total Recall: Understanding Traffic Signs using Deep Hierarchical Convolutional Neural Networks
Recognizing Traffic Signs using intelligent systems can drastically reduce
the number of accidents happening world-wide. With the arrival of Self-driving
cars it has become a staple challenge to solve the automatic recognition of
Traffic and Hand-held signs in the major streets. Various machine learning
techniques like Random Forest, SVM as well as deep learning models has been
proposed for classifying traffic signs. Though they reach state-of-the-art
performance on a particular data-set, but fall short of tackling multiple
Traffic Sign Recognition benchmarks. In this paper, we propose a novel and
one-for-all architecture that aces multiple benchmarks with better overall
score than the state-of-the-art architectures. Our model is made of residual
convolutional blocks with hierarchical dilated skip connections joined in
steps. With this we score 99.33% Accuracy in German sign recognition benchmark
and 99.17% Accuracy in Belgian traffic sign classification benchmark. Moreover,
we propose a newly devised dilated residual learning representation technique
which is very low in both memory and computational complexity
How to Solve Classification and Regression Problems on High-Dimensional Data with a Supervised Extension of Slow Feature Analysis
Supervised learning from high-dimensional data, e.g., multimedia data, is a challenging task. We propose an extension of slow feature analysis (SFA) for supervised dimensionality reduction called graph-based SFA (GSFA). The algorithm extracts a label-predictive low-dimensional set of features that can be post-processed by typical supervised algorithms to generate the final label or class estimation. GSFA is trained with a so-called training graph, in which the vertices are the samples and the edges represent similarities of the corresponding labels. A new weighted SFA optimization problem is introduced, generalizing the notion of slowness from sequences of samples to such training graphs. We show that GSFA computes an optimal solution to this problem in the considered function space, and propose several types of training graphs. For classification, the most straightforward graph yields features equivalent to those of (nonlinear) Fisher discriminant analysis. Emphasis is on regression, where four different graphs were evaluated experimentally with a subproblem of face detection on photographs. The method proposed is promising particularly when linear models are insufficient, as well as when feature selection is difficult
Learning scale-variant and scale-invariant features for deep image classification
Convolutional Neural Networks (CNNs) require large image corpora to be
trained on classification tasks. The variation in image resolutions, sizes of
objects and patterns depicted, and image scales, hampers CNN training and
performance, because the task-relevant information varies over spatial scales.
Previous work attempting to deal with such scale variations focused on
encouraging scale-invariant CNN representations. However, scale-invariant
representations are incomplete representations of images, because images
contain scale-variant information as well. This paper addresses the combined
development of scale-invariant and scale-variant representations. We propose a
multi- scale CNN method to encourage the recognition of both types of features
and evaluate it on a challenging image classification task involving
task-relevant characteristics at multiple scales. The results show that our
multi-scale CNN outperforms single-scale CNN. This leads to the conclusion that
encouraging the combined development of a scale-invariant and scale-variant
representation in CNNs is beneficial to image recognition performance
Incremental multi-domain learning with network latent tensor factorization
The prominence of deep learning, large amount of annotated data and
increasingly powerful hardware made it possible to reach remarkable performance
for supervised classification tasks, in many cases saturating the training
sets. However the resulting models are specialized to a single very specific
task and domain. Adapting the learned classification to new domains is a hard
problem due to at least three reasons: (1) the new domains and the tasks might
be drastically different; (2) there might be very limited amount of annotated
data on the new domain and (3) full training of a new model for each new task
is prohibitive in terms of computation and memory, due to the sheer number of
parameters of deep CNNs. In this paper, we present a method to learn
new-domains and tasks incrementally, building on prior knowledge from already
learned tasks and without catastrophic forgetting. We do so by jointly
parametrizing weights across layers using low-rank Tucker structure. The core
is task agnostic while a set of task specific factors are learnt on each new
domain. We show that leveraging tensor structure enables better performance
than simply using matrix operations. Joint tensor modelling also naturally
leverages correlations across different layers. Compared with previous methods
which have focused on adapting each layer separately, our approach results in
more compact representations for each new task/domain. We apply the proposed
method to the 10 datasets of the Visual Decathlon Challenge and show that our
method offers on average about 7.5x reduction in number of parameters and
competitive performance in terms of both classification accuracy and Decathlon
score.Comment: AAAI2
A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community
In recent years, deep learning (DL), a re-branding of neural networks (NNs),
has risen to the top in numerous areas, namely computer vision (CV), speech
recognition, natural language processing, etc. Whereas remote sensing (RS)
possesses a number of unique challenges, primarily related to sensors and
applications, inevitably RS draws from many of the same theories as CV; e.g.,
statistics, fusion, and machine learning, to name a few. This means that the RS
community should be aware of, if not at the leading edge of, of advancements
like DL. Herein, we provide the most comprehensive survey of state-of-the-art
RS DL research. We also review recent new developments in the DL field that can
be used in DL for RS. Namely, we focus on theories, tools and challenges for
the RS community. Specifically, we focus on unsolved challenges and
opportunities as it relates to (i) inadequate data sets, (ii)
human-understandable solutions for modelling physical phenomena, (iii) Big
Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and
learning algorithms for spectral, spatial and temporal data, (vi) transfer
learning, (vii) an improved theoretical understanding of DL systems, (viii)
high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote
Sensin
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