  Multi-modal reasoning systems rely on a pre-trained object detector to
extract regions of interest from the image. However, this crucial module is
typically used as a black box, trained independently of the downstream task and
on a fixed vocabulary of objects and attributes. This makes it challenging for
such systems to capture the long tail of visual concepts expressed in free form
text. In this paper we propose MDETR, an end-to-end modulated detector that
detects objects in an image conditioned on a raw text query, like a caption or
a question. We use a transformer-based architecture to reason jointly over text
and image by fusing the two modalities at an early stage of the model. We
pre-train the network on 1.3M text-image pairs, mined from pre-existing
multi-modal datasets having explicit alignment between phrases in text and
objects in the image. We then fine-tune on several downstream tasks such as
phrase grounding, referring expression comprehension and segmentation,
achieving state-of-the-art results on popular benchmarks. We also investigate
the utility of our model as an object detector on a given label set when
fine-tuned in a few-shot setting. We show that our pre-training approach
provides a way to handle the long tail of object categories which have very few
labelled instances. Our approach can be easily extended for visual question
answering, achieving competitive performance on GQA and CLEVR. The code and
models are available at https://github.com/ashkamath/mdetr.
