  The reference-based object segmentation tasks, namely referring image
segmentation (RIS), few-shot image segmentation (FSS), 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 four
reference-based object segmentation tasks with a single architecture. At the
heart of our approach is the proposed UniFusion module which performs
multiway-fusion for handling different tasks with respect to their specified
references. And a unified Transformer architecture is then adopted for
achieving instance-level segmentation. With the unified designs, UniRef++ can
be jointly trained on a broad range of benchmarks and can flexibly complete
multiple tasks at run-time by specifying the corresponding references. We
evaluate our unified models 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 FSS and VOS with a
parameter-shared network. Moreover, we showcase that the proposed UniFusion
module could be easily incorporated into the current advanced foundation model
SAM and obtain satisfactory results with parameter-efficient finetuning. Codes
and models are available at \url{https://github.com/FoundationVision/UniRef}.
