Intra-Camera Supervised Person Re-Identification
- URL: http://arxiv.org/abs/2002.05046v3
- Date: Sat, 16 Jan 2021 06:55:06 GMT
- Title: Intra-Camera Supervised Person Re-Identification
- Authors: Xiangping Zhu, Xiatian Zhu, Minxian Li, Pietro Morerio, Vittorio
Murino, and Shaogang Gong
- Abstract summary: We propose a novel person re-identification paradigm based on an idea of independent per-camera identity annotation.
This eliminates the most time-consuming and tedious inter-camera identity labelling process.
We formulate a Multi-tAsk mulTi-labEl (MATE) deep learning method for Intra-Camera Supervised (ICS) person re-id.
- Score: 87.88852321309433
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Existing person re-identification (re-id) methods mostly exploit a large set
of cross-camera identity labelled training data. This requires a tedious data
collection and annotation process, leading to poor scalability in practical
re-id applications. On the other hand unsupervised re-id methods do not need
identity label information, but they usually suffer from much inferior and
insufficient model performance. To overcome these fundamental limitations, we
propose a novel person re-identification paradigm based on an idea of
independent per-camera identity annotation. This eliminates the most
time-consuming and tedious inter-camera identity labelling process,
significantly reducing the amount of human annotation efforts. Consequently, it
gives rise to a more scalable and more feasible setting, which we call
Intra-Camera Supervised (ICS) person re-id, for which we formulate a Multi-tAsk
mulTi-labEl (MATE) deep learning method. Specifically, MATE is designed for
self-discovering the cross-camera identity correspondence in a per-camera
multi-task inference framework. Extensive experiments demonstrate the
cost-effectiveness superiority of our method over the alternative approaches on
three large person re-id datasets. For example, MATE yields 88.7% rank-1 score
on Market-1501 in the proposed ICS person re-id setting, significantly
outperforming unsupervised learning models and closely approaching conventional
fully supervised learning competitors.
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