AgentRVOS: Reasoning over Object Tracks for Zero-Shot Referring Video Object Segmentation
This work addresses the challenge of segmenting objects in videos based on language queries without training, offering a more effective solution for video analysis applications.
The paper tackles the problem of zero-shot referring video object segmentation by proposing AgentRVOS, a training-free pipeline that uses SAM3 for mask tracks and an MLLM for reasoning, achieving state-of-the-art performance among training-free methods on multiple benchmarks.
Referring Video Object Segmentation (RVOS) aims to segment a target object throughout a video given a natural language query. Training-free methods for this task follow a common pipeline: a MLLM selects keyframes, grounds the referred object within those frames, and a video segmentation model propagates the results. While intuitive, this design asks the MLLM to make temporal decisions before any object-level evidence is available, limiting both reasoning quality and spatio-temporal coverage. To overcome this, we propose AgentRVOS, a training-free agentic pipeline built on the complementary strengths of SAM3 and a MLLM. Given a concept derived from the query, SAM3 provides reliable perception over the full spatio-temporal extent through generated mask tracks. The MLLM then identifies the target through query-grounded reasoning over this object-level evidence, iteratively pruning guided by SAM3's temporal existence information. Extensive experiments show that AgentRVOS achieves state-of-the-art performance among training-free methods across multiple benchmarks, with consistent results across diverse MLLM backbones. Our project page is available at: https://cvlab-kaist.github.io/AgentRVOS/.