  Visual grounding (VG) aims at locating the foreground entities that match the
given natural language expressions. Previous datasets and methods for classic
VG task mainly rely on the prior assumption that the given expression must
literally refer to the target object, which greatly impedes the practical
deployment of agents in real-world scenarios. Since users usually prefer to
provide intention-based expression for the desired object instead of covering
all the details, it is necessary for the agents to interpret the
intention-driven instructions. Thus, in this work, we take a step further to
the intention-driven visual-language (V-L) understanding. To promote classic VG
towards human intention interpretation, we propose a new intention-driven
visual grounding (IVG) task and build a large-scale IVG dataset termed
IntentionVG with free-form intention expressions. Considering that practical
agents need to move and find specific targets among various scenarios to
realize the grounding task, our IVG task and IntentionVG dataset have taken the
crucial properties of both multi-scenario perception and egocentric view into
consideration. Besides, various types of models are set up as the baselines to
realize our IVG task. Extensive experiments on our IntentionVG dataset and
baselines demonstrate the necessity and efficacy of our method for the V-L
field. To foster future research in this direction, our newly built dataset and
baselines will be publicly available at https://github.com/Rubics-Xuan/IVG.
