1 research outputs found
Weakly Supervised Tracklet Person Re-Identification by Deep Feature-wise Mutual Learning
The scalability problem caused by the difficulty in annotating Person
Re-identification(Re-ID) datasets has become a crucial bottleneck in the
development of Re-ID.To address this problem, many unsupervised Re-ID methods
have recently been proposed.Nevertheless, most of these models require transfer
from another auxiliary fully supervised dataset, which is still expensive to
obtain.In this work, we propose a Re-ID model based on Weakly Supervised
Tracklets(WST) data from various camera views, which can be inexpensively
acquired by combining the fragmented tracklets of the same person in the same
camera view over a period of time.We formulate our weakly supervised tracklets
Re-ID model by a novel method, named deep feature-wise mutual learning(DFML),
which consists of Mutual Learning on Feature Extractors (MLFE) and Mutual
Learning on Feature Classifiers (MLFC).We propose MLFE by leveraging two
feature extractors to learn from each other to extract more robust and
discriminative features.On the other hand, we propose MLFC by adapting
discriminative features from various camera views to each classifier. Extensive
experiments demonstrate the superiority of our proposed DFML over the
state-of-the-art unsupervised models and even some supervised models on three
Re-ID benchmark datasets