Object segmentation in images and videos is a complex yet crucial process. In the past, different tasks like referring image segmentation (RIS), few-shot image segmentation (FSS), referring video object segmentation (RVOS), and video object segmentation (VOS) have developed independently. This led to inefficiencies, making it hard to utilize multi-task learning.
UniRef++, designed by researchers from The University of Hong Kong, ByteDance, Dalian University of Technology, and Shanghai AI Laboratory, aims to bridge these gaps. It’s a unified architecture with the unique UniFusion module, which can handle tasks based on their specific references.
Unlike other benchmarks, UniRef++ can be taught across various activities, absorbing broad information that can be used for different roles. It has shown competitive outcomes in FSS and VOS and superior performance in RIS and RVOS tasks. UniRef++’s flexibility allows it to carry out numerous functions at runtime with the correct references specified. This new approach sets a new standard in the field, offering insights and directions for future research and development in the domain of object segmentation.
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