  Open-world video instance segmentation is an important video understanding
task. Yet most methods either operate in a closed-world setting, require an
additional user-input, or use classic region-based proposals to identify never
before seen objects. Further, these methods only assign a one-word label to
detected objects, and don't generate rich object-centric descriptions. They
also often suffer from highly overlapping predictions. To address these issues,
we propose Open-World Video Instance Segmentation and Captioning (OW-VISCap),
an approach to jointly segment, track, and caption previously seen or unseen
objects in a video. For this, we introduce open-world object queries to
discover never before seen objects without additional user-input. We generate
rich and descriptive object-centric captions for each detected object via a
masked attention augmented LLM input. We introduce an inter-query contrastive
loss to ensure that the object queries differ from one another. Our generalized
approach matches or surpasses state-of-the-art on three tasks: open-world video
instance segmentation on the BURST dataset, dense video object captioning on
the VidSTG dataset, and closed-world video instance segmentation on the OVIS
dataset.
