234 research outputs found
Implicit Integration of Superpixel Segmentation into Fully Convolutional Networks
Superpixels are a useful representation to reduce the complexity of image
data. However, to combine superpixels with convolutional neural networks (CNNs)
in an end-to-end fashion, one requires extra models to generate superpixels and
special operations such as graph convolution. In this paper, we propose a way
to implicitly integrate a superpixel scheme into CNNs, which makes it easy to
use superpixels with CNNs in an end-to-end fashion. Our proposed method
hierarchically groups pixels at downsampling layers and generates superpixels.
Our method can be plugged into many existing architectures without a change in
their feed-forward path because our method does not use superpixels in the
feed-forward path but use them to recover the lost resolution instead of
bilinear upsampling. As a result, our method preserves detailed information
such as object boundaries in the form of superpixels even when the model
contains downsampling layers. We evaluate our method on several tasks such as
semantic segmentation, superpixel segmentation, and monocular depth estimation,
and confirm that it speeds up modern architectures and/or improves their
prediction accuracy in these tasks
Adversarial Transformations for Semi-Supervised Learning
We propose a Regularization framework based on Adversarial Transformations
(RAT) for semi-supervised learning. RAT is designed to enhance robustness of
the output distribution of class prediction for a given data against input
perturbation. RAT is an extension of Virtual Adversarial Training (VAT) in such
a way that RAT adversarialy transforms data along the underlying data
distribution by a rich set of data transformation functions that leave class
label invariant, whereas VAT simply produces adversarial additive noises. In
addition, we verified that a technique of gradually increasing of perturbation
region further improve the robustness. In experiments, we show that RAT
significantly improves classification performance on CIFAR-10 and SVHN compared
to existing regularization methods under standard semi-supervised image
classification settings.Comment: Accepted by AAAI 202
Drive Video Analysis for the Detection of Traffic Near-Miss Incidents
Because of their recent introduction, self-driving cars and advanced driver
assistance system (ADAS) equipped vehicles have had little opportunity to
learn, the dangerous traffic (including near-miss incident) scenarios that
provide normal drivers with strong motivation to drive safely. Accordingly, as
a means of providing learning depth, this paper presents a novel traffic
database that contains information on a large number of traffic near-miss
incidents that were obtained by mounting driving recorders in more than 100
taxis over the course of a decade. The study makes the following two main
contributions: (i) In order to assist automated systems in detecting near-miss
incidents based on database instances, we created a large-scale traffic
near-miss incident database (NIDB) that consists of video clip of dangerous
events captured by monocular driving recorders. (ii) To illustrate the
applicability of NIDB traffic near-miss incidents, we provide two primary
database-related improvements: parameter fine-tuning using various near-miss
scenes from NIDB, and foreground/background separation into motion
representation. Then, using our new database in conjunction with a monocular
driving recorder, we developed a near-miss recognition method that provides
automated systems with a performance level that is comparable to a human-level
understanding of near-miss incidents (64.5% vs. 68.4% at near-miss recognition,
61.3% vs. 78.7% at near-miss detection).Comment: Accepted to ICRA 201
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