UVIS: Unsupervised Video Instance Segmentation
Proceedings of the IEEE/CVF Conference on Computer Vision and …, 2024•openaccess.thecvf.com
Video instance segmentation requires classifying segmenting and tracking every object
across video frames. Unlike existing approaches that rely on masks boxes or category labels
we propose UVIS a novel Unsupervised Video Instance Segmentation (UVIS) framework
that can perform video instance segmentation without any video annotations or dense label-
based pretraining. Our key insight comes from leveraging the dense shape prior from the
self-supervised vision foundation model DINO and the open set recognition ability from the …
across video frames. Unlike existing approaches that rely on masks boxes or category labels
we propose UVIS a novel Unsupervised Video Instance Segmentation (UVIS) framework
that can perform video instance segmentation without any video annotations or dense label-
based pretraining. Our key insight comes from leveraging the dense shape prior from the
self-supervised vision foundation model DINO and the open set recognition ability from the …
Abstract
Video instance segmentation requires classifying segmenting and tracking every object across video frames. Unlike existing approaches that rely on masks boxes or category labels we propose UVIS a novel Unsupervised Video Instance Segmentation (UVIS) framework that can perform video instance segmentation without any video annotations or dense label-based pretraining. Our key insight comes from leveraging the dense shape prior from the self-supervised vision foundation model DINO and the open set recognition ability from the image-caption supervised vision-language model CLIP. Our UVIS framework consists of three essential steps: frame-level pseudo-label generation transformer-based VIS model training and query-based tracking. To improve the quality of VIS predictions in the unsupervised setup we introduce a dual-memory design. This design includes a semantic memory bank for generating accurate pseudo-labels and a tracking memory bank for maintaining temporal consistency in object tracks. We evaluate our approach on three standard VIS benchmarks namely YoutubeVIS-2019 YoutubeVIS-2021 and Occluded VIS. Our UVIS achieves 21.1 AP on YoutubeVIS-2019 without any video annotations or dense pretraining demonstrating the potential of our unsupervised VIS framework.
openaccess.thecvf.com