  Existing visual tracking methods typically take an image patch as the
reference of the target to perform tracking. However, a single image patch
cannot provide a complete and precise concept of the target object as images
are limited in their ability to abstract and can be ambiguous, which makes it
difficult to track targets with drastic variations. In this paper, we propose
the CiteTracker to enhance target modeling and inference in visual tracking by
connecting images and text. Specifically, we develop a text generation module
to convert the target image patch into a descriptive text containing its class
and attribute information, providing a comprehensive reference point for the
target. In addition, a dynamic description module is designed to adapt to
target variations for more effective target representation. We then associate
the target description and the search image using an attention-based
correlation module to generate the correlated features for target state
reference. Extensive experiments on five diverse datasets are conducted to
evaluate the proposed algorithm and the favorable performance against the
state-of-the-art methods demonstrates the effectiveness of the proposed
tracking method.
