435 research outputs found
Visible-Infrared Person Re-Identification Using Privileged Intermediate Information
Visible-infrared person re-identification (ReID) aims to recognize a same
person of interest across a network of RGB and IR cameras. Some deep learning
(DL) models have directly incorporated both modalities to discriminate persons
in a joint representation space. However, this cross-modal ReID problem remains
challenging due to the large domain shift in data distributions between RGB and
IR modalities. % This paper introduces a novel approach for a creating
intermediate virtual domain that acts as bridges between the two main domains
(i.e., RGB and IR modalities) during training. This intermediate domain is
considered as privileged information (PI) that is unavailable at test time, and
allows formulating this cross-modal matching task as a problem in learning
under privileged information (LUPI). We devised a new method to generate images
between visible and infrared domains that provide additional information to
train a deep ReID model through an intermediate domain adaptation. In
particular, by employing color-free and multi-step triplet loss objectives
during training, our method provides common feature representation spaces that
are robust to large visible-infrared domain shifts. % Experimental results on
challenging visible-infrared ReID datasets indicate that our proposed approach
consistently improves matching accuracy, without any computational overhead at
test time. The code is available at:
\href{https://github.com/alehdaghi/Cross-Modal-Re-ID-via-LUPI}{https://github.com/alehdaghi/Cross-Modal-Re-ID-via-LUPI
Multi-Modal 3D Object Detection in Autonomous Driving: a Survey
In the past few years, we have witnessed rapid development of autonomous
driving. However, achieving full autonomy remains a daunting task due to the
complex and dynamic driving environment. As a result, self-driving cars are
equipped with a suite of sensors to conduct robust and accurate environment
perception. As the number and type of sensors keep increasing, combining them
for better perception is becoming a natural trend. So far, there has been no
indepth review that focuses on multi-sensor fusion based perception. To bridge
this gap and motivate future research, this survey devotes to review recent
fusion-based 3D detection deep learning models that leverage multiple sensor
data sources, especially cameras and LiDARs. In this survey, we first introduce
the background of popular sensors for autonomous cars, including their common
data representations as well as object detection networks developed for each
type of sensor data. Next, we discuss some popular datasets for multi-modal 3D
object detection, with a special focus on the sensor data included in each
dataset. Then we present in-depth reviews of recent multi-modal 3D detection
networks by considering the following three aspects of the fusion: fusion
location, fusion data representation, and fusion granularity. After a detailed
review, we discuss open challenges and point out possible solutions. We hope
that our detailed review can help researchers to embark investigations in the
area of multi-modal 3D object detection
Adversarial Self-Attack Defense and Spatial-Temporal Relation Mining for Visible-Infrared Video Person Re-Identification
In visible-infrared video person re-identification (re-ID), extracting
features not affected by complex scenes (such as modality, camera views,
pedestrian pose, background, etc.) changes, and mining and utilizing motion
information are the keys to solving cross-modal pedestrian identity matching.
To this end, the paper proposes a new visible-infrared video person re-ID
method from a novel perspective, i.e., adversarial self-attack defense and
spatial-temporal relation mining. In this work, the changes of views, posture,
background and modal discrepancy are considered as the main factors that cause
the perturbations of person identity features. Such interference information
contained in the training samples is used as an adversarial perturbation. It
performs adversarial attacks on the re-ID model during the training to make the
model more robust to these unfavorable factors. The attack from the adversarial
perturbation is introduced by activating the interference information contained
in the input samples without generating adversarial samples, and it can be thus
called adversarial self-attack. This design allows adversarial attack and
defense to be integrated into one framework. This paper further proposes a
spatial-temporal information-guided feature representation network to use the
information in video sequences. The network cannot only extract the information
contained in the video-frame sequences but also use the relation of the local
information in space to guide the network to extract more robust features. The
proposed method exhibits compelling performance on large-scale cross-modality
video datasets. The source code of the proposed method will be released at
https://github.com/lhf12278/xxx.Comment: 11 pages,8 figure
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