[Submitted on 15 Oct 2026]
Title:Event-STVG: Spatio-Temporal Video Grounding for Long-Form Event Reasoning
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Abstract:Spatio-Temporal Video Grounding (STVG) aims to jointly localize a target in space and time given a natural-language query. Despite continued advances, existing STVG benchmarks remain largely centered on localizing targets through instance-specific descriptions, leaving them largely decoupled from long-form reasoning increasingly emphasized in multimodal large language models (MLLMs). In this paper, we introduce Event-STVG, a task that shifts STVG from locating what is described toward event-level reasoning by unifying long-form context, repeated-event reasoning, event localization, role inference, and multi-target grounding. To facilitate research on Event-STVG, we construct a large-scale benchmark comprising 4,016 annotated event occurrences across soccer and seven additional domains. Experiments with state-of-the-art MLLMs reveal substantial difficulty, with the strongest model achieving only 5.4% m-vIoU at the 10-minute horizon. Across domains, we consistently observe confusion among repeated same-type events, spatial grounding bottlenecks, and rare success in jointly grounding all required roles. Event-STVG establishes a new challenge for visual understanding in MLLMs, calling for future models that can jointly reason about complex events across space and time. Code and annotations are available at this https URL
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2610.22444 [cs.CV]
(or arXiv:2610.22444v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2610.22444
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Yizhe Zhang [view email] [v1] Thu, 15 Oct 2026 15:13:54 UTC (48,396 KB)
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