Multi-Model Induced Source-free Video Domain Adaptation

Ruizhe Li, Jiangbo Pei, Li Xiao, Aidong Men

Keywords: Domain Adaptation, Multi-Task Learning

Abstract:

Existing Source-free Video Domain Adaptation (SFVDA) aims to learn a target video model for an unlabeled target domain by transferring knowledge from a labeled source domain using a single pre-trained source video model. In this paper, we explore a new SFVDA setting where multiple source domains exist, each offering a library of source models with different architectures. This setting offers both opportunities and challenges: while the presence of multiple source models enriches the pool of transferable knowledge, it also increases the risk of negative transfer due to inappropriate source knowledge. To tackle these challenges, we introduce the Multiple-Source-Video-Model Aggregation Framework (MSVMA), comprising two key modules. The first module, termed Multi-level Instance Transferability Calibration (MITC), enhances existing uncertainty-based transferability estimation metrics by incorporating scale information from both group and dataset levels. This integration facilitates accurate transferability estimation at the instance level across diverse models. The second module, termed Instance-level Multi Video Model Aggregation (IMVMA), leverages the calculated instance-level transferability to guide a path generation network. This network produces instance-specific weights for unsupervised aggregation of source models. Empirical results from three video domain adaptation datasets demonstrate the state-of-the-art performance of our MSVMA framework.

Primary Area: transfer learning, meta learning, and lifelong learning

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Submission Number: 3045