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Tracking any given object(s) spatially and temporally is a common purpose inVisual Object Tracking (VOT) and Video Object Segmentation (VOS). Jointtracking and segmentation have been attempted in some studies but they oftenlack full compatibility of both box and mask in initialization and prediction,and mainly focus on single-object scenarios. To address these limitations, thispaper proposes a Multi-object Mask-box Integrated framework for unifiedTracking and Segmentation, dubbed MITS. Firstly, the unified identificationmodule is proposed to support both box and mask reference for initialization,where detailed object information is inferred from boxes or directly retainedfrom masks. Additionally, a novel pinpoint box predictor is proposed foraccurate multi-object box prediction, facilitating target-orientedrepresentation learning. All target objects are processed simultaneously fromencoding to propagation and decoding, as a unified pipeline for VOT and VOS.Experimental results show MITS achieves state-of-the-art performance on bothVOT and VOS benchmarks. Notably, MITS surpasses the best prior VOT competitorby around 6% on the GOT-10k test set, and significantly improves theperformance of box initialization on VOS benchmarks. The code is available athttps://github.com/yoxu515/MITS.

Integrating Boxes and Masks: A Multi-Object Framework for Unified Visual Tracking and Segmentation