2,271 research outputs found
Colorization as a Proxy Task for Visual Understanding
We investigate and improve self-supervision as a drop-in replacement for
ImageNet pretraining, focusing on automatic colorization as the proxy task.
Self-supervised training has been shown to be more promising for utilizing
unlabeled data than other, traditional unsupervised learning methods. We build
on this success and evaluate the ability of our self-supervised network in
several contexts. On VOC segmentation and classification tasks, we present
results that are state-of-the-art among methods not using ImageNet labels for
pretraining representations.
Moreover, we present the first in-depth analysis of self-supervision via
colorization, concluding that formulation of the loss, training details and
network architecture play important roles in its effectiveness. This
investigation is further expanded by revisiting the ImageNet pretraining
paradigm, asking questions such as: How much training data is needed? How many
labels are needed? How much do features change when fine-tuned? We relate these
questions back to self-supervision by showing that colorization provides a
similarly powerful supervisory signal as various flavors of ImageNet
pretraining.Comment: CVPR 2017 (Project page:
http://people.cs.uchicago.edu/~larsson/color-proxy/
Multi-scale Orderless Pooling of Deep Convolutional Activation Features
Deep convolutional neural networks (CNN) have shown their promise as a
universal representation for recognition. However, global CNN activations lack
geometric invariance, which limits their robustness for classification and
matching of highly variable scenes. To improve the invariance of CNN
activations without degrading their discriminative power, this paper presents a
simple but effective scheme called multi-scale orderless pooling (MOP-CNN).
This scheme extracts CNN activations for local patches at multiple scale
levels, performs orderless VLAD pooling of these activations at each level
separately, and concatenates the result. The resulting MOP-CNN representation
can be used as a generic feature for either supervised or unsupervised
recognition tasks, from image classification to instance-level retrieval; it
consistently outperforms global CNN activations without requiring any joint
training of prediction layers for a particular target dataset. In absolute
terms, it achieves state-of-the-art results on the challenging SUN397 and MIT
Indoor Scenes classification datasets, and competitive results on
ILSVRC2012/2013 classification and INRIA Holidays retrieval datasets
Feature Learning from Spectrograms for Assessment of Personality Traits
Several methods have recently been proposed to analyze speech and
automatically infer the personality of the speaker. These methods often rely on
prosodic and other hand crafted speech processing features extracted with
off-the-shelf toolboxes. To achieve high accuracy, numerous features are
typically extracted using complex and highly parameterized algorithms. In this
paper, a new method based on feature learning and spectrogram analysis is
proposed to simplify the feature extraction process while maintaining a high
level of accuracy. The proposed method learns a dictionary of discriminant
features from patches extracted in the spectrogram representations of training
speech segments. Each speech segment is then encoded using the dictionary, and
the resulting feature set is used to perform classification of personality
traits. Experiments indicate that the proposed method achieves state-of-the-art
results with a significant reduction in complexity when compared to the most
recent reference methods. The number of features, and difficulties linked to
the feature extraction process are greatly reduced as only one type of
descriptors is used, for which the 6 parameters can be tuned automatically. In
contrast, the simplest reference method uses 4 types of descriptors to which 6
functionals are applied, resulting in over 20 parameters to be tuned.Comment: 12 pages, 3 figure
On the use of SIFT features for face authentication
Several pattern recognition and classification techniques
have been applied to the biometrics domain. Among them,
an interesting technique is the Scale Invariant Feature
Transform (SIFT), originally devised for object recognition.
Even if SIFT features have emerged as a very powerful image
descriptors, their employment in face analysis context
has never been systematically investigated.
This paper investigates the application of the SIFT approach
in the context of face authentication. In order to determine
the real potential and applicability of the method,
different matching schemes are proposed and tested using
the BANCA database and protocol, showing promising results
Review of Person Re-identification Techniques
Person re-identification across different surveillance cameras with disjoint
fields of view has become one of the most interesting and challenging subjects
in the area of intelligent video surveillance. Although several methods have
been developed and proposed, certain limitations and unresolved issues remain.
In all of the existing re-identification approaches, feature vectors are
extracted from segmented still images or video frames. Different similarity or
dissimilarity measures have been applied to these vectors. Some methods have
used simple constant metrics, whereas others have utilised models to obtain
optimised metrics. Some have created models based on local colour or texture
information, and others have built models based on the gait of people. In
general, the main objective of all these approaches is to achieve a
higher-accuracy rate and lowercomputational costs. This study summarises
several developments in recent literature and discusses the various available
methods used in person re-identification. Specifically, their advantages and
disadvantages are mentioned and compared.Comment: Published 201
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