1,664 research outputs found
Pedestrian Attribute Recognition: A Survey
Recognizing pedestrian attributes is an important task in computer vision
community due to it plays an important role in video surveillance. Many
algorithms has been proposed to handle this task. The goal of this paper is to
review existing works using traditional methods or based on deep learning
networks. Firstly, we introduce the background of pedestrian attributes
recognition (PAR, for short), including the fundamental concepts of pedestrian
attributes and corresponding challenges. Secondly, we introduce existing
benchmarks, including popular datasets and evaluation criterion. Thirdly, we
analyse the concept of multi-task learning and multi-label learning, and also
explain the relations between these two learning algorithms and pedestrian
attribute recognition. We also review some popular network architectures which
have widely applied in the deep learning community. Fourthly, we analyse
popular solutions for this task, such as attributes group, part-based,
\emph{etc}. Fifthly, we shown some applications which takes pedestrian
attributes into consideration and achieve better performance. Finally, we
summarized this paper and give several possible research directions for
pedestrian attributes recognition. The project page of this paper can be found
from the following website:
\url{https://sites.google.com/view/ahu-pedestrianattributes/}.Comment: Check our project page for High Resolution version of this survey:
https://sites.google.com/view/ahu-pedestrianattributes
Weakly-supervised Part-Attention and Mentored Networks for Vehicle Re-Identification
Vehicle re-identification (Re-ID) aims to retrieve images with the same
vehicle ID across different cameras. Current part-level feature learning
methods typically detect vehicle parts via uniform division, outside tools, or
attention modeling. However, such part features often require expensive
additional annotations and cause sub-optimal performance in case of unreliable
part mask predictions. In this paper, we propose a weakly-supervised
Part-Attention Network (PANet) and Part-Mentored Network (PMNet) for Vehicle
Re-ID. Firstly, PANet localizes vehicle parts via part-relevant channel
recalibration and cluster-based mask generation without vehicle part
supervisory information. Secondly, PMNet leverages teacher-student guided
learning to distill vehicle part-specific features from PANet and performs
multi-scale global-part feature extraction. During inference, PMNet can
adaptively extract discriminative part features without part localization by
PANet, preventing unstable part mask predictions. We address this Re-ID issue
as a multi-task problem and adopt Homoscedastic Uncertainty to learn the
optimal weighing of ID losses. Experiments are conducted on two public
benchmarks, showing that our approach outperforms recent methods, which require
no extra annotations by an average increase of 3.0% in CMC@5 on VehicleID and
over 1.4% in mAP on VeRi776. Moreover, our method can extend to the occluded
vehicle Re-ID task and exhibits good generalization ability.Comment: This work has been submitted to the IEEE for possible publication.
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