  Recently, one-stage visual grounders attract high attention due to their
comparable accuracy but significantly higher efficiency than two-stage
grounders. However, inter-object relation modeling has not been well studied
for one-stage grounders. Inter-object relationship modeling, though important,
is not necessarily performed among all objects, as only part of them are
related to the text query and may confuse the model. We call these objects
suspected objects. However, exploring their relationships in the one-stage
paradigm is non-trivial because: First, no object proposals are available as
the basis on which to select suspected objects and perform relationship
modeling. Second, suspected objects are more confusing than others, as they may
share similar semantics, be entangled with certain relationships, etc, and
thereby more easily mislead the model prediction. Toward this end, we propose a
Suspected Object Transformation mechanism (SOT), which can be seamlessly
integrated into existing CNN and Transformer-based one-stage visual grounders
to encourage the target object selection among the suspected ones. Suspected
objects are dynamically discovered from a learned activation map adapted to the
model current discrimination ability during training. Afterward, on top of
suspected objects, a Keyword-Aware Discrimination module (KAD) and an
Exploration by Random Connection strategy (ERC) are concurrently proposed to
help the model rethink its initial prediction. On the one hand, KAD leverages
keywords contributing high to suspected object discrimination. On the other
hand, ERC allows the model to seek the correct object instead of being trapped
in a situation that always exploits the current false prediction. Extensive
experiments demonstrate the effectiveness of our proposed method.
