  Controllable Image Captioning (CIC) -- generating image descriptions
following designated control signals -- has received unprecedented attention
over the last few years. To emulate the human ability in controlling caption
generation, current CIC studies focus exclusively on control signals concerning
objective properties, such as contents of interest or descriptive patterns.
However, we argue that almost all existing objective control signals have
overlooked two indispensable characteristics of an ideal control signal: 1)
Event-compatible: all visual contents referred to in a single sentence should
be compatible with the described activity. 2) Sample-suitable: the control
signals should be suitable for a specific image sample. To this end, we propose
a new control signal for CIC: Verb-specific Semantic Roles (VSR). VSR consists
of a verb and some semantic roles, which represents a targeted activity and the
roles of entities involved in this activity. Given a designated VSR, we first
train a grounded semantic role labeling (GSRL) model to identify and ground all
entities for each role. Then, we propose a semantic structure planner (SSP) to
learn human-like descriptive semantic structures. Lastly, we use a role-shift
captioning model to generate the captions. Extensive experiments and ablations
demonstrate that our framework can achieve better controllability than several
strong baselines on two challenging CIC benchmarks. Besides, we can generate
multi-level diverse captions easily. The code is available at:
https://github.com/mad-red/VSR-guided-CIC.
