# Annotation-Free Temporal Self-Supervised Representation Learning

Comparing polyps in colonoscopy depends on complementary views taken from different angles and imaging conditions, yet recent contrastive methods still build polyp tracklet representations from manually annotated bounding boxes. An annotation-free framework constructs tracklet views directly from raw colonoscopy videos and runs temporal self-supervised learning on full-procedure videos, including frames with no visible polyp. On REAL-Colon it beats both self-supervised and supervised baselines on tracklet retrieval and re-identification, and in few-shot polyp retrieval, a task closer to clinical use, two examples surpass most competing methods given ten.

Comparing polyps in colonoscopy depends on complementary views taken from different angles and imaging conditions, yet recent contrastive methods still build polyp tracklet representations from manually annotated bounding boxes. An annotation-free framework constructs tracklet views directly from raw colonoscopy videos and runs temporal self-supervised learning on full-procedure videos, including frames with no visible polyp. On REAL-Colon it beats both self-supervised and supervised baselines on tracklet retrieval and re-identification, and in few-shot polyp retrieval, a task closer to clinical use, two examples surpass most competing methods given ten.