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arXiv · 2607.24098

ReflexTrack: A Feedback-Driven Agent for Training-Free Referring Video Object Segmentation

Yuanjia Li·Tianyang Xu·Tao Zhou·Zhangyong Tang·Xiao-Jun Wu·Josef Kittler
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Abstract

Referring video object segmentation (RVOS) requires segmenting a target specified by natural language throughout a video. Recent agentic approaches combine multimodal large language models with promptable segmentation models to perform RVOS without task-specific training. However, most pipelines rely on one-shot spatial grounding followed by mask propagation, leaving both the initial prompts and temporal predictions largely unverified. We introduce ReflexTrack, a training-free, feedback-driven agent that closes this loop at both spatial and temporal levels. Mask-guided Spatial Refinement evaluates the mask induced by the current keyframe prompt and iteratively updates the bounding box together with positive and negative points, yielding a more reliable initialization. Video-level Mask Reflection assesses the complete mask sequence, localizes unreliable intervals, selects complementary repair keyframes, and generates candidate predictions through mask-guided re-propagation. Only candidates that provide a verified improvement are used to update the affected intervals, preserving reliable predictions elsewhere. All components remain frozen during inference. ReflexTrack achieves an overall $\mathcal{Q}$ score of $69.7$ on Ref-VPS and a $\mathcal{J}\&\mathcal{F}$ score of $67.2$ on ReasonVOS. These results demonstrate that prediction-level feedback substantially improves the reliability of training-free RVOS.

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Yuanjia Li, Tianyang Xu, Tao Zhou, Zhangyong Tang, Xiao-Jun Wu, Josef Kittler. 2026-07-27. ReflexTrack: A Feedback-Driven Agent for Training-Free Referring Video Object Segmentation. https://arxiv.org/abs/2607.24098

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