Learning to cope with domain change has been known
as a challenging problem in many real-world applications.
This paper proposes a novel and efficient approach, named
domain adaptive semantic diffusion (DASD), to exploit
semantic context while considering the domain-shift-ofcontext
for large scale video concept annotation. Starting
with a large set of concept detectors, the proposed DASD
refines the initial annotation results using graph diffusion
technique, which preserves the consistency and smoothness
of the annotation over a semantic graph. Different from
the existing graph learning methods which capture relations
among data samples, the semantic graph treats concepts
as nodes and the concept affinities as the weights of
edges. Particularly, the DASD approach is capable of simultaneously
improving the annotation results and adapting
the concept affinities to new test data. The adaptation
provides a means to handle domain change between training
and test data, which...