  We focus on the problem of segmenting a certain object referred by a natural
language sentence in video content, at the core of formulating a pinpoint
vision-language relation. While existing attempts mainly construct such
relation in an implicit way, i.e., grid-level multi-modal feature fusion, it
has been proven problematic to distinguish semantically similar objects under
this paradigm. In this work, we propose to interwind the visual and linguistic
modalities in an explicit way via the contrastive learning objective, which
directly aligns the referred object and the language description and separates
the unreferred content apart across frames. Moreover, to remedy for the
degradation problem, we present two complementary hard instance mining
strategies, i.e., Language-relevant Channel Filter and Relative Hard Instance
Construction. They encourage the network to exclude visual-distinguishable
feature and to focus on easy-confused objects during the contrastive training.
Extensive experiments on two benchmarks, i.e., A2D Sentences and J-HMDB
Sentences, quantitatively demonstrate the state-of-the-arts performance of our
method and qualitatively show the more accurate distinguishment between
semantically similar objects over baselines.
