  Partial Domain Adaptation (PDA) is a practical and general domain adaptation
scenario, which relaxes the fully shared label space assumption such that the
source label space subsumes the target one. The key challenge of PDA is the
issue of negative transfer caused by source-only classes. For videos, such
negative transfer could be triggered by both spatial and temporal features,
which leads to a more challenging Partial Video Domain Adaptation (PVDA)
problem. In this paper, we propose a novel Partial Adversarial Temporal
Attentive Network (PATAN) to address the PVDA problem by utilizing both spatial
and temporal features for filtering source-only classes. Besides, PATAN
constructs effective overall temporal features by attending to local temporal
features that contribute more toward the class filtration process. We further
introduce new benchmarks to facilitate research on PVDA problems, covering a
wide range of PVDA scenarios. Empirical results demonstrate the
state-of-the-art performance of our proposed PATAN across the multiple PVDA
benchmarks.
