In this paper, we propose a self-supervised RGB-T tracking method. Different
from existing deep RGB-T trackers that use a large number of annotated RGB-T
image pairs for training, our RGB-T tracker is trained using unlabeled RGB-T
video pairs in a self-supervised manner. We propose a novel cross-input
consistency-based self-supervised training strategy based on the idea that
tracking can be performed using different inputs. Specifically, we construct
two distinct inputs using unlabeled RGB-T video pairs. We then track objects
using these two inputs to generate results, based on which we construct our
cross-input consistency loss. Meanwhile, we propose a reweighting strategy to
make our loss function robust to low-quality training samples. We build our
tracker on a Siamese correlation filter network. To the best of our knowledge,
our tracker is the first self-supervised RGB-T tracker. Extensive experiments
on two public RGB-T tracking benchmarks demonstrate that the proposed training
strategy is effective. Remarkably, despite training only with a corpus of
unlabeled RGB-T video pairs, our tracker outperforms seven supervised RGB-T
trackers on the GTOT dataset.

本研究提出了一种自监督的 RGB-T 跟踪方法，使用跨输入一致性自监督训练策略和重新加权策略来提高训练效率，基于 Siamese 相关滤波网络建立跟踪器，表现优于七个有监督 RGB-T 跟踪器。

带有交叉一致性的自监督 RGB-T 跟踪

Self-Supervised RGB-T Tracking with Cross-Input Consistency

In this paper, we propose a self-supervised learning procedure for training a
robust multi-object tracking (MOT) model given only unlabeled video. While
several self-supervisory learning signals have been proposed in prior work on
single-object tracking, such as color propagation and cycle-consistency, these
signals cannot be directly applied for training RNN models, which are needed to
achieve accurate MOT: they yield degenerate models that, for instance, always
match new detections to tracks with the closest initial detections. We propose
a novel self-supervisory signal that we call cross-input consistency: we
construct two distinct inputs for the same sequence of video, by hiding
different information about the sequence in each input. We then compute tracks
in that sequence by applying an RNN model independently on each input, and
train the model to produce consistent tracks across the two inputs. We evaluate
our unsupervised method on MOT17 and KITTI -- remarkably, we find that, despite
training only on unlabeled video, our unsupervised approach outperforms four
supervised methods published in the last 1--2 years, including Tracktor++,
FAMNet, GSM, and mmMOT.

本文提出了一种自监督学习方法，用于训练仅提供无标签视频的多目标跟踪模型，通过交叉输入一致性的方法，在未经过监督的情况下优于多种经过监督的方法。