  Answering query with semantic concepts has long been the mainstream approach
for video search. Until recently, its performance is surpassed by concept-free
approach, which embeds queries in a joint space as videos. Nevertheless, the
embedded features as well as search results are not interpretable, hindering
subsequent steps in video browsing and query reformulation. This paper
integrates feature embedding and concept interpretation into a neural network
for unified dual-task learning. In this way, an embedding is associated with a
list of semantic concepts as an interpretation of video content. This paper
empirically demonstrates that, by using either the embedding features or
concepts, considerable search improvement is attainable on TRECVid benchmarked
datasets. Concepts are not only effective in pruning false positive videos, but
also highly complementary to concept-free search, leading to large margin of
improvement compared to state-of-the-art approaches.
