4,270 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
ECL: Class-Enhancement Contrastive Learning for Long-tailed Skin Lesion Classification
Skin image datasets often suffer from imbalanced data distribution,
exacerbating the difficulty of computer-aided skin disease diagnosis. Some
recent works exploit supervised contrastive learning (SCL) for this long-tailed
challenge. Despite achieving significant performance, these SCL-based methods
focus more on head classes, yet ignoring the utilization of information in tail
classes. In this paper, we propose class-Enhancement Contrastive Learning
(ECL), which enriches the information of minority classes and treats different
classes equally. For information enhancement, we design a hybrid-proxy model to
generate class-dependent proxies and propose a cycle update strategy for
parameters optimization. A balanced-hybrid-proxy loss is designed to exploit
relations between samples and proxies with different classes treated equally.
Taking both "imbalanced data" and "imbalanced diagnosis difficulty" into
account, we further present a balanced-weighted cross-entropy loss following
curriculum learning schedule. Experimental results on the classification of
imbalanced skin lesion data have demonstrated the superiority and effectiveness
of our method
FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning
Pseudo labeling and consistency regularization approaches based on confidencethresholding have made great progress in semi-supervised learning (SSL).However, we argue that existing methods might fail to adopt suitable thresholdssince they either use a pre-defined / fixed threshold or an ad-hoc thresholdadjusting scheme, resulting in inferior performance and slow convergence. Wefirst analyze a motivating example to achieve some intuitions on therelationship between the desirable threshold and model's learning status. Basedon the analysis, we hence propose FreeMatch to define and adjust the confidencethreshold in a self-adaptive manner according to the model's learning status.We further introduce a self-adaptive class fairness regularization penalty thatencourages the model to produce diverse predictions during the early stages oftraining. Extensive experimental results indicate the superiority of FreeMatchespecially when the labeled data are extremely rare. FreeMatch achieves 5.78%,13.59%, and 1.28% error rate reduction over the latest state-of-the-art methodFlexMatch on CIFAR-10 with 1 label per class, STL-10 with 4 labels per class,and ImageNet with 100 labels per class, respectively.<br
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