JIST: Joint Image and Sequence Training for Sequential Visual Place
  Recognition
Gabriele Berton Gabriele Trivigno Barbara Caputo Carlo Masone
Visual Place Recognition aims at recognizing previously visited places by
relying on visual clues, and it is used in robotics applications for SLAM and
localization. Since typically a mobile robot has access to a continuous stream
of frames, this task is naturally cast as a sequence-to-sequence localization
problem. Nevertheless, obtaining sequences of labelled data is much more
expensive than collecting isolated images, which can be done in an automated
way with little supervision. As a mitigation to this problem, we propose a
novel Joint Image and Sequence Training protocol (JIST) that leverages large
uncurated sets of images through a multi-task learning framework. With JIST we
also introduce SeqGeM, an aggregation layer that revisits the popular GeM
pooling to produce a single robust and compact embedding from a sequence of
single-frame embeddings. We show that our model is able to outperform previous
state of the art while being faster, using 8 times smaller descriptors, having
a lighter architecture and allowing to process sequences of various lengths.
Code is available at https://github.com/ga1i13o/JIST