Grounding in the Dark: Text-Only Training for Zero-Shot Video Temporal Localization

arXiv:2611.06149v2 Announce Type: replace-cross Abstract: Video-LLMs are typically trained on paired video-text data for temporal tasks, yet collecting precise temporal annotations and training on large-scale video datasets are both costly. We investigate whether a text-only LLM, with no vision-language pretraining, can be trained on textual video descriptions alone to perform temporal grounding and receive video only at inference time. This setting removes paired video data from training altogether and is a step toward training multimodal models without paired annotations. We generate a 134K-

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