  Long-tailed object detection (LTOD) aims to handle the extreme data imbalance
in real-world datasets, where many tail classes have scarce instances. One
popular strategy is to explore extra data with image-level labels, yet it
produces limited results due to (1) semantic ambiguity -- an image-level label
only captures a salient part of the image, ignoring the remaining rich
semantics within the image; and (2) location sensitivity -- the label highly
depends on the locations and crops of the original image, which may change
after data transformations like random cropping. To remedy this, we propose
RichSem, a simple but effective method, which is robust to learn rich semantics
from coarse locations without the need of accurate bounding boxes. RichSem
leverages rich semantics from images, which are then served as additional soft
supervision for training detectors. Specifically, we add a semantic branch to
our detector to learn these soft semantics and enhance feature representations
for long-tailed object detection. The semantic branch is only used for training
and is removed during inference. RichSem achieves consistent improvements on
both overall and rare-category of LVIS under different backbones and detectors.
Our method achieves state-of-the-art performance without requiring complex
training and testing procedures. Moreover, we show the effectiveness of our
method on other long-tailed datasets with additional experiments. Code is
available at \url{https://github.com/MengLcool/RichSem}.
