Segment Every Reference Object in Spatial and Temporal Spaces

The reference-based object segmentation tasks, namely referring image segmentation (RIS), referring video object segmentation (RVOS), and video object segmentation (VOS), aim to segment a specific object by utilizing either language or annotated masks as references. Despite significant progress in each respective field, current methods are task-specifically designed and developed in different directions, which hinders the activation of multi-task capabilities for these tasks. In this work, we end the current fragmented situation and propose UniRef to unify the three reference-based object segmentation tasks with a single architecture. At the heart of our approach is the multiway-fusion for handling different task with respect to their specified references. And a unified Transformer architecture is then adopted for performing instance-level segmentation. With the unified designs, UniRef can be jointly trained on a broad range of benchmarks and can flexibly perform multiple tasks at runtime by specifying the corresponding references. We evaluate the jointly trained network on various benchmarks. Extensive experimental results indicate that our proposed UniRef achieves state-of-the-art performance on RIS and RVOS, and performs competitively on VOS with a single network.