Long-RVOS: A Comprehensive Benchmark for Long-term Referring Video Object Segmentation

TL;DR

This paper introduces Long-RVOS, a large-scale benchmark dataset for long-term referring video object segmentation, highlighting the challenges of long videos and proposing a new baseline method, ReferMo, to improve performance.

Contribution

The paper presents Long-RVOS, a comprehensive long-duration video dataset with new evaluation metrics and introduces ReferMo, a baseline method that effectively captures long-term dependencies in RVOS.

Findings

Current methods perform poorly on long videos.

ReferMo significantly outperforms existing approaches in long-term scenarios.

Long-RVOS enables more realistic evaluation of RVOS models.

Abstract

Referring video object segmentation (RVOS) aims to identify, track and segment the objects in a video based on language descriptions, which has received great attention in recent years. However, existing datasets remain focus on short video clips within several seconds, with salient objects visible in most frames. To advance the task towards more practical scenarios, we introduce \textbf{Long-RVOS}, a large-scale benchmark for long-term referring video object segmentation. Long-RVOS contains 2,000+ videos of an average duration exceeding 60 seconds, covering a variety of objects that undergo occlusion, disappearance-reappearance and shot changing. The objects are manually annotated with three different types of descriptions to individually evaluate the understanding of static attributes, motion patterns and spatiotemporal relationships. Moreover, unlike previous benchmarks that rely solely on the per-frame spatial evaluation, we introduce two new metrics to assess the temporal and spatiotemporal consistency. We benchmark 6 state-of-the-art methods on Long-RVOS. The results show that current approaches struggle severely with the long-video challenges. To address this, we further propose ReferMo, a promising baseline method that integrates motion information to expand the temporal receptive field, and employs a local-to-global architecture to capture both short-term dynamics and long-term dependencies. Despite simplicity, ReferMo achieves significant improvements over current methods in long-term scenarios. We hope that Long-RVOS and our baseline can drive future RVOS research towards tackling more realistic and long-form videos.