6,894 research outputs found
Ambient Sound Helps: Audiovisual Crowd Counting in Extreme Conditions
Visual crowd counting has been recently studied as a way to enable people
counting in crowd scenes from images. Albeit successful, vision-based crowd
counting approaches could fail to capture informative features in extreme
conditions, e.g., imaging at night and occlusion. In this work, we introduce a
novel task of audiovisual crowd counting, in which visual and auditory
information are integrated for counting purposes. We collect a large-scale
benchmark, named auDiovISual Crowd cOunting (DISCO) dataset, consisting of
1,935 images and the corresponding audio clips, and 170,270 annotated
instances. In order to fuse the two modalities, we make use of a linear
feature-wise fusion module that carries out an affine transformation on visual
and auditory features. Finally, we conduct extensive experiments using the
proposed dataset and approach. Experimental results show that introducing
auditory information can benefit crowd counting under different illumination,
noise, and occlusion conditions. The dataset and code will be released. Code
and data have been made availabl
SA-Net: Deep Neural Network for Robot Trajectory Recognition from RGB-D Streams
Learning from demonstration (LfD) and imitation learning offer new paradigms
for transferring task behavior to robots. A class of methods that enable such
online learning require the robot to observe the task being performed and
decompose the sensed streaming data into sequences of state-action pairs, which
are then input to the methods. Thus, recognizing the state-action pairs
correctly and quickly in sensed data is a crucial prerequisite for these
methods. We present SA-Net a deep neural network architecture that recognizes
state-action pairs from RGB-D data streams. SA-Net performed well in two
diverse robotic applications of LfD -- one involving mobile ground robots and
another involving a robotic manipulator -- which demonstrates that the
architecture generalizes well to differing contexts. Comprehensive evaluations
including deployment on a physical robot show that \sanet{} significantly
improves on the accuracy of the previous method that utilizes traditional image
processing and segmentation.Comment: (in press
Robustness of 3D Deep Learning in an Adversarial Setting
Understanding the spatial arrangement and nature of real-world objects is of
paramount importance to many complex engineering tasks, including autonomous
navigation. Deep learning has revolutionized state-of-the-art performance for
tasks in 3D environments; however, relatively little is known about the
robustness of these approaches in an adversarial setting. The lack of
comprehensive analysis makes it difficult to justify deployment of 3D deep
learning models in real-world, safety-critical applications. In this work, we
develop an algorithm for analysis of pointwise robustness of neural networks
that operate on 3D data. We show that current approaches presented for
understanding the resilience of state-of-the-art models vastly overestimate
their robustness. We then use our algorithm to evaluate an array of
state-of-the-art models in order to demonstrate their vulnerability to
occlusion attacks. We show that, in the worst case, these networks can be
reduced to 0% classification accuracy after the occlusion of at most 6.5% of
the occupied input space.Comment: 10 pages, 8 figures, 1 tabl
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