  Progress on object detection is enabled by datasets that focus the research
community's attention on open challenges. This process led us from simple
images to complex scenes and from bounding boxes to segmentation masks. In this
work, we introduce LVIS (pronounced `el-vis'): a new dataset for Large
Vocabulary Instance Segmentation. We plan to collect ~2 million high-quality
instance segmentation masks for over 1000 entry-level object categories in 164k
images. Due to the Zipfian distribution of categories in natural images, LVIS
naturally has a long tail of categories with few training samples. Given that
state-of-the-art deep learning methods for object detection perform poorly in
the low-sample regime, we believe that our dataset poses an important and
exciting new scientific challenge. LVIS is available at
http://www.lvisdataset.org.
