Decentralised Person Re-Identification with Selective Knowledge Aggregation. (arXiv:2110.11384v1 [cs.CV])

Existing person re-identification (Re-ID) methods mostly follow a centralised
learning paradigm which shares all training data to a collection for model
learning. This paradigm is limited when data from different sources cannot be
shared due to privacy concerns. To resolve this problem, two recent works have
introduced decentralised (federated) Re-ID learning for constructing a globally
generalised model (server)without any direct access to local training data nor
shared data across different source domains (clients). However, these methods
are poor on how to adapt the generalised model to maximise its performance on
individual client domain Re-ID tasks having different Re-ID label spaces, due
to a lack of understanding of data heterogeneity across domains. We call this
poor ‘model personalisation’. In this work, we present a new Selective
Knowledge Aggregation approach to decentralised person Re-ID to optimise the
trade-off between model personalisation and generalisation. Specifically, we
incorporate attentive normalisation into the normalisation layers in a deep
ReID model and propose to learn local normalisation layers specific to each
domain, which are decoupled from the global model aggregation in federated
Re-ID learning. This helps to preserve model personalisation knowledge on each
local client domain and learn instance-specific information. Further, we
introduce a dual local normalisation mechanism to learn generalised
normalisation layers in each local model, which are then transmitted to the
global model for central aggregation. This facilitates selective knowledge
aggregation on the server to construct a global generalised model for
out-of-the-box deployment on unseen novel domains. Extensive experiments on
eight person Re-ID datasets show that the proposed approach to decentralised
Re-ID significantly outperforms the state-of-the-art decentralised methods.

Source: https://arxiv.org/abs/2110.11384

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