  Referring Expressions Generation (REG) aims to produce textual descriptions
that unambiguously identifies specific objects within a visual scene.
Traditionally, this has been achieved through supervised learning methods,
which perform well on specific data distributions but often struggle to
generalize to new images and concepts. To address this issue, we present a
novel approach for REG, named DisCLIP, short for discriminative CLIP. We build
on CLIP, a large-scale visual-semantic model, to guide an LLM to generate a
contextual description of a target concept in an image while avoiding other
distracting concepts. Notably, this optimization happens at inference time and
does not require additional training or tuning of learned parameters. We
measure the quality of the generated text by evaluating the capability of a
receiver model to accurately identify the described object within the scene. To
achieve this, we use a frozen zero-shot comprehension module as a critique of
our generated referring expressions. We evaluate DisCLIP on multiple referring
expression benchmarks through human evaluation and show that it significantly
outperforms previous methods on out-of-domain datasets. Our results highlight
the potential of using pre-trained visual-semantic models for generating
high-quality contextual descriptions.
