  We propose a method to efficiently equip the Segment Anything Model (SAM)
with the ability to generate regional captions. SAM presents strong
generalizability to segment anything while is short for semantic understanding.
By introducing a lightweight query-based feature mixer, we align the
region-specific features with the embedding space of language models for later
caption generation. As the number of trainable parameters is small (typically
in the order of tens of millions), it costs less computation, less memory
usage, and less communication bandwidth, resulting in both fast and scalable
training. To address the scarcity problem of regional caption data, we propose
to first pre-train our model on objection detection and segmentation tasks. We
call this step weak supervision pretraining since the pre-training data only
contains category names instead of full-sentence descriptions. The weak
supervision pretraining allows us to leverage many publicly available object
detection and segmentation datasets. We conduct extensive experiments to
demonstrate the superiority of our method and validate each design choice. This
work serves as a stepping stone towards scaling up regional captioning data and
sheds light on exploring efficient ways to augment SAM with regional semantics.
The project page, along with the associated code, can be accessed via
https://xk-huang.github.io/segment-caption-anything/.
