  Colonoscopic Polyp Re-Identification aims to match a specific polyp in a
large gallery with different cameras and views, which plays a key role for the
prevention and treatment of colorectal cancer in the computer-aided diagnosis.
However, traditional methods mainly focus on the visual representation
learning, while neglect to explore the potential of semantic features during
training, which may easily leads to poor generalization capability when adapted
the pretrained model into the new scenarios. To relieve this dilemma, we
propose a simple but effective training method named VT-ReID, which can
remarkably enrich the representation of polyp videos with the interchange of
high-level semantic information. Moreover, we elaborately design a novel
clustering mechanism to introduce prior knowledge from textual data, which
leverages contrastive learning to promote better separation from abundant
unlabeled text data. To the best of our knowledge, this is the first attempt to
employ the visual-text feature with clustering mechanism for the colonoscopic
polyp re-identification. Empirical results show that our method significantly
outperforms current state-of-the art methods with a clear margin.
