  The recent progress in language-based open-vocabulary object detection can be
largely attributed to finding better ways of leveraging large-scale data with
free-form text annotations. Training such models with a discriminative
objective function has proven successful, but requires good positive and
negative samples. However, the free-form nature and the open vocabulary of
object descriptions make the space of negatives extremely large. Prior works
randomly sample negatives or use rule-based techniques to build them. In
contrast, we propose to leverage the vast knowledge built into modern
generative models to automatically build negatives that are more relevant to
the original data. Specifically, we use large-language-models to generate
negative text descriptions, and text-to-image diffusion models to also generate
corresponding negative images. Our experimental analysis confirms the relevance
of the generated negative data, and its use in language-based detectors
improves performance on two complex benchmarks. Code is available at
\url{https://github.com/xiaofeng94/Gen-Enhanced-Negs}.
