13,757 research outputs found
When Face Recognition Meets with Deep Learning: an Evaluation of Convolutional Neural Networks for Face Recognition
Deep learning, in particular Convolutional Neural Network (CNN), has achieved
promising results in face recognition recently. However, it remains an open
question: why CNNs work well and how to design a 'good' architecture. The
existing works tend to focus on reporting CNN architectures that work well for
face recognition rather than investigate the reason. In this work, we conduct
an extensive evaluation of CNN-based face recognition systems (CNN-FRS) on a
common ground to make our work easily reproducible. Specifically, we use public
database LFW (Labeled Faces in the Wild) to train CNNs, unlike most existing
CNNs trained on private databases. We propose three CNN architectures which are
the first reported architectures trained using LFW data. This paper
quantitatively compares the architectures of CNNs and evaluate the effect of
different implementation choices. We identify several useful properties of
CNN-FRS. For instance, the dimensionality of the learned features can be
significantly reduced without adverse effect on face recognition accuracy. In
addition, traditional metric learning method exploiting CNN-learned features is
evaluated. Experiments show two crucial factors to good CNN-FRS performance are
the fusion of multiple CNNs and metric learning. To make our work reproducible,
source code and models will be made publicly available.Comment: 7 pages, 4 figures, 7 table
Fine-graind Image Classification via Combining Vision and Language
Fine-grained image classification is a challenging task due to the large
intra-class variance and small inter-class variance, aiming at recognizing
hundreds of sub-categories belonging to the same basic-level category. Most
existing fine-grained image classification methods generally learn part
detection models to obtain the semantic parts for better classification
accuracy. Despite achieving promising results, these methods mainly have two
limitations: (1) not all the parts which obtained through the part detection
models are beneficial and indispensable for classification, and (2)
fine-grained image classification requires more detailed visual descriptions
which could not be provided by the part locations or attribute annotations. For
addressing the above two limitations, this paper proposes the two-stream model
combining vision and language (CVL) for learning latent semantic
representations. The vision stream learns deep representations from the
original visual information via deep convolutional neural network. The language
stream utilizes the natural language descriptions which could point out the
discriminative parts or characteristics for each image, and provides a flexible
and compact way of encoding the salient visual aspects for distinguishing
sub-categories. Since the two streams are complementary, combining the two
streams can further achieves better classification accuracy. Comparing with 12
state-of-the-art methods on the widely used CUB-200-2011 dataset for
fine-grained image classification, the experimental results demonstrate our CVL
approach achieves the best performance.Comment: 9 pages, to appear in CVPR 201
Color Constancy Convolutional Autoencoder
In this paper, we study the importance of pre-training for the generalization
capability in the color constancy problem. We propose two novel approaches
based on convolutional autoencoders: an unsupervised pre-training algorithm
using a fine-tuned encoder and a semi-supervised pre-training algorithm using a
novel composite-loss function. This enables us to solve the data scarcity
problem and achieve competitive, to the state-of-the-art, results while
requiring much fewer parameters on ColorChecker RECommended dataset. We further
study the over-fitting phenomenon on the recently introduced version of
INTEL-TUT Dataset for Camera Invariant Color Constancy Research, which has both
field and non-field scenes acquired by three different camera models.Comment: 6 pages, 1 figure, 3 table
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