73,548 research outputs found
DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
In this work we address the task of semantic image segmentation with Deep
Learning and make three main contributions that are experimentally shown to
have substantial practical merit. First, we highlight convolution with
upsampled filters, or 'atrous convolution', as a powerful tool in dense
prediction tasks. Atrous convolution allows us to explicitly control the
resolution at which feature responses are computed within Deep Convolutional
Neural Networks. It also allows us to effectively enlarge the field of view of
filters to incorporate larger context without increasing the number of
parameters or the amount of computation. Second, we propose atrous spatial
pyramid pooling (ASPP) to robustly segment objects at multiple scales. ASPP
probes an incoming convolutional feature layer with filters at multiple
sampling rates and effective fields-of-views, thus capturing objects as well as
image context at multiple scales. Third, we improve the localization of object
boundaries by combining methods from DCNNs and probabilistic graphical models.
The commonly deployed combination of max-pooling and downsampling in DCNNs
achieves invariance but has a toll on localization accuracy. We overcome this
by combining the responses at the final DCNN layer with a fully connected
Conditional Random Field (CRF), which is shown both qualitatively and
quantitatively to improve localization performance. Our proposed "DeepLab"
system sets the new state-of-art at the PASCAL VOC-2012 semantic image
segmentation task, reaching 79.7% mIOU in the test set, and advances the
results on three other datasets: PASCAL-Context, PASCAL-Person-Part, and
Cityscapes. All of our code is made publicly available online.Comment: Accepted by TPAM
TossingBot: Learning to Throw Arbitrary Objects with Residual Physics
We investigate whether a robot arm can learn to pick and throw arbitrary
objects into selected boxes quickly and accurately. Throwing has the potential
to increase the physical reachability and picking speed of a robot arm.
However, precisely throwing arbitrary objects in unstructured settings presents
many challenges: from acquiring reliable pre-throw conditions (e.g. initial
pose of object in manipulator) to handling varying object-centric properties
(e.g. mass distribution, friction, shape) and dynamics (e.g. aerodynamics). In
this work, we propose an end-to-end formulation that jointly learns to infer
control parameters for grasping and throwing motion primitives from visual
observations (images of arbitrary objects in a bin) through trial and error.
Within this formulation, we investigate the synergies between grasping and
throwing (i.e., learning grasps that enable more accurate throws) and between
simulation and deep learning (i.e., using deep networks to predict residuals on
top of control parameters predicted by a physics simulator). The resulting
system, TossingBot, is able to grasp and throw arbitrary objects into boxes
located outside its maximum reach range at 500+ mean picks per hour (600+
grasps per hour with 85% throwing accuracy); and generalizes to new objects and
target locations. Videos are available at https://tossingbot.cs.princeton.eduComment: Summary Video: https://youtu.be/f5Zn2Up2RjQ Project webpage:
https://tossingbot.cs.princeton.ed
Attentive Single-Tasking of Multiple Tasks
In this work we address task interference in universal networks by
considering that a network is trained on multiple tasks, but performs one task
at a time, an approach we refer to as "single-tasking multiple tasks". The
network thus modifies its behaviour through task-dependent feature adaptation,
or task attention. This gives the network the ability to accentuate the
features that are adapted to a task, while shunning irrelevant ones. We further
reduce task interference by forcing the task gradients to be statistically
indistinguishable through adversarial training, ensuring that the common
backbone architecture serving all tasks is not dominated by any of the
task-specific gradients. Results in three multi-task dense labelling problems
consistently show: (i) a large reduction in the number of parameters while
preserving, or even improving performance and (ii) a smooth trade-off between
computation and multi-task accuracy. We provide our system's code and
pre-trained models at http://vision.ee.ethz.ch/~kmaninis/astmt/.Comment: CVPR 2019 Camera Read
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