  Retrieving tracked-vehicles by natural language descriptions plays a critical
role in smart city construction. It aims to find the best match for the given
texts from a set of tracked vehicles in surveillance videos. Existing works
generally solve it by a dual-stream framework, which consists of a text
encoder, a visual encoder and a cross-modal loss function. Although some
progress has been made, they failed to fully exploit the information at various
levels of granularity. To tackle this issue, we propose a novel framework for
the natural language-based vehicle retrieval task, OMG, which Observes Multiple
Granularities with respect to visual representation, textual representation and
objective functions. For the visual representation, target features, context
features and motion features are encoded separately. For the textual
representation, one global embedding, three local embeddings and a color-type
prompt embedding are extracted to represent various granularities of semantic
features. Finally, the overall framework is optimized by a cross-modal
multi-granularity contrastive loss function. Experiments demonstrate the
effectiveness of our method. Our OMG significantly outperforms all previous
methods and ranks the 9th on the 6th AI City Challenge Track2. The codes are
available at https://github.com/dyhBUPT/OMG.
