89 research outputs found
Deep Self-Taught Learning for Weakly Supervised Object Localization
Most existing weakly supervised localization (WSL) approaches learn detectors
by finding positive bounding boxes based on features learned with image-level
supervision. However, those features do not contain spatial location related
information and usually provide poor-quality positive samples for training a
detector. To overcome this issue, we propose a deep self-taught learning
approach, which makes the detector learn the object-level features reliable for
acquiring tight positive samples and afterwards re-train itself based on them.
Consequently, the detector progressively improves its detection ability and
localizes more informative positive samples. To implement such self-taught
learning, we propose a seed sample acquisition method via image-to-object
transferring and dense subgraph discovery to find reliable positive samples for
initializing the detector. An online supportive sample harvesting scheme is
further proposed to dynamically select the most confident tight positive
samples and train the detector in a mutual boosting way. To prevent the
detector from being trapped in poor optima due to overfitting, we propose a new
relative improvement of predicted CNN scores for guiding the self-taught
learning process. Extensive experiments on PASCAL 2007 and 2012 show that our
approach outperforms the state-of-the-arts, strongly validating its
effectiveness.Comment: Accepted as spotlight paper by CVPR 201
Perceptual Generative Adversarial Networks for Small Object Detection
Detecting small objects is notoriously challenging due to their low
resolution and noisy representation. Existing object detection pipelines
usually detect small objects through learning representations of all the
objects at multiple scales. However, the performance gain of such ad hoc
architectures is usually limited to pay off the computational cost. In this
work, we address the small object detection problem by developing a single
architecture that internally lifts representations of small objects to
"super-resolved" ones, achieving similar characteristics as large objects and
thus more discriminative for detection. For this purpose, we propose a new
Perceptual Generative Adversarial Network (Perceptual GAN) model that improves
small object detection through narrowing representation difference of small
objects from the large ones. Specifically, its generator learns to transfer
perceived poor representations of the small objects to super-resolved ones that
are similar enough to real large objects to fool a competing discriminator.
Meanwhile its discriminator competes with the generator to identify the
generated representation and imposes an additional perceptual requirement -
generated representations of small objects must be beneficial for detection
purpose - on the generator. Extensive evaluations on the challenging
Tsinghua-Tencent 100K and the Caltech benchmark well demonstrate the
superiority of Perceptual GAN in detecting small objects, including traffic
signs and pedestrians, over well-established state-of-the-arts
Deep Learning with S-shaped Rectified Linear Activation Units
Rectified linear activation units are important components for
state-of-the-art deep convolutional networks. In this paper, we propose a novel
S-shaped rectified linear activation unit (SReLU) to learn both convex and
non-convex functions, imitating the multiple function forms given by the two
fundamental laws, namely the Webner-Fechner law and the Stevens law, in
psychophysics and neural sciences. Specifically, SReLU consists of three
piecewise linear functions, which are formulated by four learnable parameters.
The SReLU is learned jointly with the training of the whole deep network
through back propagation. During the training phase, to initialize SReLU in
different layers, we propose a "freezing" method to degenerate SReLU into a
predefined leaky rectified linear unit in the initial several training epochs
and then adaptively learn the good initial values. SReLU can be universally
used in the existing deep networks with negligible additional parameters and
computation cost. Experiments with two popular CNN architectures, Network in
Network and GoogLeNet on scale-various benchmarks including CIFAR10, CIFAR100,
MNIST and ImageNet demonstrate that SReLU achieves remarkable improvement
compared to other activation functions.Comment: Accepted by AAAI-1
Reversible Recursive Instance-level Object Segmentation
In this work, we propose a novel Reversible Recursive Instance-level Object
Segmentation (R2-IOS) framework to address the challenging instance-level
object segmentation task. R2-IOS consists of a reversible proposal refinement
sub-network that predicts bounding box offsets for refining the object proposal
locations, and an instance-level segmentation sub-network that generates the
foreground mask of the dominant object instance in each proposal. By being
recursive, R2-IOS iteratively optimizes the two sub-networks during joint
training, in which the refined object proposals and improved segmentation
predictions are alternately fed into each other to progressively increase the
network capabilities. By being reversible, the proposal refinement sub-network
adaptively determines an optimal number of refinement iterations required for
each proposal during both training and testing. Furthermore, to handle multiple
overlapped instances within a proposal, an instance-aware denoising autoencoder
is introduced into the segmentation sub-network to distinguish the dominant
object from other distracting instances. Extensive experiments on the
challenging PASCAL VOC 2012 benchmark well demonstrate the superiority of
R2-IOS over other state-of-the-art methods. In particular, the
over classes at IoU achieves , which significantly
outperforms the results of by PFN~\cite{PFN} and
by~\cite{liu2015multi}.Comment: 9 page
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