1 research outputs found
Cluster and Aggregate: Face Recognition with Large Probe Set
Feature fusion plays a crucial role in unconstrained face recognition where
inputs (probes) comprise of a set of low quality images whose individual
qualities vary. Advances in attention and recurrent modules have led to feature
fusion that can model the relationship among the images in the input set.
However, attention mechanisms cannot scale to large due to their quadratic
complexity and recurrent modules suffer from input order sensitivity. We
propose a two-stage feature fusion paradigm, Cluster and Aggregate, that can
both scale to large and maintain the ability to perform sequential
inference with order invariance. Specifically, Cluster stage is a linear
assignment of inputs to global cluster centers, and Aggregation stage
is a fusion over clustered features. The clustered features play an
integral role when the inputs are sequential as they can serve as a
summarization of past features. By leveraging the order-invariance of
incremental averaging operation, we design an update rule that achieves
batch-order invariance, which guarantees that the contributions of early image
in the sequence do not diminish as time steps increase. Experiments on IJB-B
and IJB-S benchmark datasets show the superiority of the proposed two-stage
paradigm in unconstrained face recognition. Code and pretrained models are
available in https://github.com/mk-minchul/cafaceComment: To appear in NeurIPS 202