  The task of RGBT tracking aims to take the complementary advantages from
visible spectrum and thermal infrared data to achieve robust visual tracking,
and receives more and more attention in recent years. Existing works focus on
modality-specific information integration by introducing modality weights to
achieve adaptive fusion or learning robust feature representations of different
modalities. Although these methods could effectively deploy the
modality-specific properties, they ignore the potential values of
modality-shared cues as well as instance-aware information, which are crucial
for effective fusion of different modalities in RGBT tracking. In this paper,
we propose a novel Multi-Adapter convolutional Network (MANet) to jointly
perform modality-shared, modality-specific and instance-aware feature learning
in an end-to-end trained deep framework for RGBT tracking. We design three
kinds of adapters within our network. In a specific, the generality adapter is
to extract shared object representations, the modality adapter aims at encoding
modality-specific information to deploy their complementary advantages, and the
instance adapter is to model the appearance properties and temporal variations
of a certain object. Moreover, to reduce computational complexity for real-time
demand of visual tracking, we design a parallel structure of generic adapter
and modality adapter. Extensive experiments on two RGBT tracking benchmark
datasets demonstrate the outstanding performance of the proposed tracker
against other state-of-the-art RGB and RGBT tracking algorithms.
