  To solve video-and-language grounding tasks, the key is for the network to
understand the connection between the two modalities. For a pair of video and
language description, their semantic relation is reflected by their encodings'
similarity. A good multi-modality encoder should be able to well capture both
inputs' semantics and encode them in the shared feature space where embedding
distance gets properly translated into their semantic similarity. In this work,
we focused on this semantic connection between video and language, and
developed a multi-level alignment training scheme to directly shape the
encoding process. Global and segment levels of video-language alignment pairs
were designed, based on the information similarity ranging from high-level
context to fine-grained semantics. The contrastive loss was used to contrast
the encodings' similarities between the positive and negative alignment pairs,
and to ensure the network is trained in such a way that similar information is
encoded closely in the shared feature space while information of different
semantics is kept apart. Our multi-level alignment training can be applied to
various video-and-language grounding tasks. Together with the task-specific
training loss, our framework achieved comparable performance to previous
state-of-the-arts on multiple video QA and retrieval datasets.
