  Visual grounding (VG) aims to locate a specific target in an image based on a
given language query. The discriminative information from context is important
for distinguishing the target from other objects, particularly for the targets
that have the same category as others. However, most previous methods
underestimate such information. Moreover, they are usually designed for the
standard scene (without any novel object), which limits their generalization to
the open-vocabulary scene. In this paper, we propose a novel framework with
context disentangling and prototype inheriting for robust visual grounding to
handle both scenes. Specifically, the context disentangling disentangles the
referent and context features, which achieves better discrimination between
them. The prototype inheriting inherits the prototypes discovered from the
disentangled visual features by a prototype bank to fully utilize the seen
data, especially for the open-vocabulary scene. The fused features, obtained by
leveraging Hadamard product on disentangled linguistic and visual features of
prototypes to avoid sharp adjusting the importance between the two types of
features, are then attached with a special token and feed to a vision
Transformer encoder for bounding box regression. Extensive experiments are
conducted on both standard and open-vocabulary scenes. The performance
comparisons indicate that our method outperforms the state-of-the-art methods
in both scenarios. {The code is available at
https://github.com/WayneTomas/TransCP.
