  In this paper, we introduce the Context-Aware Video Instance Segmentation
(CAVIS), a novel framework designed to enhance instance association by
integrating contextual information adjacent to each object. To efficiently
extract and leverage this information, we propose the Context-Aware Instance
Tracker (CAIT), which merges contextual data surrounding the instances with the
core instance features to improve tracking accuracy. Additionally, we introduce
the Prototypical Cross-frame Contrastive (PCC) loss, which ensures consistency
in object-level features across frames, thereby significantly enhancing
instance matching accuracy. CAVIS demonstrates superior performance over
state-of-the-art methods on all benchmark datasets in video instance
segmentation (VIS) and video panoptic segmentation (VPS). Notably, our method
excels on the OVIS dataset, which is known for its particularly challenging
videos.
