Adversarial Semi-Supervised Multi-Domain Tracking

TL;DR

This paper introduces an adversarial semi-supervised learning approach for multi-domain visual tracking, effectively separating domain-invariant and domain-specific features to improve generalization across diverse videos.

Contribution

It proposes a novel semi-supervised framework that uses adversarial learning to disentangle shared and domain-specific features, enhancing multi-domain tracking performance.

Findings

Improved tracking accuracy across multiple video domains.

Effective separation of domain-invariant and domain-specific features.

Enhanced generalization through self-supervised learning.

Abstract

Neural networks for multi-domain learning empowers an effective combination of information from different domains by sharing and co-learning the parameters. In visual tracking, the emerging features in shared layers of a multi-domain tracker, trained on various sequences, are crucial for tracking in unseen videos. Yet, in a fully shared architecture, some of the emerging features are useful only in a specific domain, reducing the generalization of the learned feature representation. We propose a semi-supervised learning scheme to separate domain-invariant and domain-specific features using adversarial learning, to encourage mutual exclusion between them, and to leverage self-supervised learning for enhancing the shared features using the unlabeled reservoir. By employing these features and training dedicated layers for each sequence, we build a tracker that performs exceptionally on different types of videos.