Self-Supervised Representation Learning is a machine learning paradigm in which models learn structured feature representations from unlabeled data by solving pretext tasks derived from the data itself, such as predicting masked tokens, contrastive instance discrimination, or reconstructing corrupted inputs. The learned representations transfer effectively to downstream tasks with limited labeled data. Methods such as contrastive learning, masked autoencoders, and DINO have driven advances across vision, language, and multimodal domains.

Posts

CALIBFREE: Self-Supervised Feature Disentanglement for Calibration-Free Multi-Camera Multi-Object Tracking

/in /by

Multi-camera multi-object tracking (MCMOT) faces significant challenges in maintaining consistent object identities across varying camera perspectives, particularly when precise calibration and extensive annotations are required. In this paper, we present CalibFree, a self-supervised representation learning framework that does not need any calibration or manual labeling for the MCMOT task. By promoting feature separation between view-agnostic and view-specific representations through single-view distillation and cross-view reconstruction, our method adapts to complex, dynamic scenarios with minimal overhead. Experiments on the MMP-MvMHAT dataset show a 3% improvement in overall accuracy and a 7.5% increase in the average F1 score over state-of-the-art approaches, confirming the effectiveness of our calibration-free design. Moreover, on the more diverse MvMHAT dataset, our approach demonstrates superior over-time tracking and strong cross-view performance, highlighting its adaptability to a wide range of camera configurations. Code will be publicly available upon acceptance.

Read more