[Submitted on 17 Oct 2026]
Title:SuperClip Pyramid for Video Temporal Grounding
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Abstract:With the rapid expansion of online video content, video temporal grounding has become essential for localizing moments based on user-specific queries, comprising two subtasks: moment retrieval (MR) and highlight detection (HD). While recent approaches adopt DETR-style decoders or temporal pyramid architectures, they rely on single-scale feature maps or uniformly downsampled representations, limiting their ability to capture semantic boundaries and variable-length events. In this paper, we present SuperClip Video Temporal Grounding (SCVTG) built on a multi-scale temporal hierarchy that better captures distinctive events at various granularities. SCVTG constructs a SuperClip pyramid by grouping adjacent clips into semantically coherent segments, termed SuperClips. Further, to aggregate these SuperClips having variable duration and temporal location, we introduce a temporally adaptive kernel that aggregates SuperClips through temporally conditioned weights, enabling flexible aggregation. On top of this, we design relative saliency losses to guide the model in learning inter-clip importance, enhancing its ability to distinguish relevant events. Extensive experiments show that SCVTG achieves the best performance across five video temporal grounding benchmarks with promising gains.
Comments: preprint
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2610.26925 [cs.CV]
(or arXiv:2610.26925v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2610.26925
arXiv-issued DOI via DataCite
Submission history
From: Sanghyeok Lee [view email] [v1] Sat, 17 Oct 2026 14:11:19 UTC (15,112 KB)
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