Natural-Language Temporal Grounding in Hour-Long Videos is a Search Problem: A Benchmark and Empirical Decomposition
arXiv:2606.12300v1 Announce Type: cross Abstract: Temporal grounding--returning the interval $[t_s, t_e]$ for a natural-language query over a video--is the language interface to long-form video, yet has been studied on short videos; the dynamics of hour-scale natural-language grounding remain underexplored. We take the position that at hour-scale, the binding constraint is search, not recognition: Video-LLMs are bottlenecked not by localizing a nearby event, but--given a natural-language query--by searching for the relevant region of a long video. To test this, we release ExtremeWhenBench, the
The increasing capability of large language models is pushing the boundaries of multimodal AI, making temporal grounding in long videos a critical next challenge.
This research addresses a key bottleneck for AI agents interacting with and understanding complex, real-world video data, which has broad applications from security to content creation.
The focus for long-video understanding shifts from mere recognition to efficient search mechanisms, challenging current Video-LLM architectures.
- · AI research labs focused on search and retrieval
- · Developers of video editing and analysis tools
- · Industries relying on long-form video content
- · Video-LLM architectures without robust search components
- · Current methods for manual video annotation
- · Legacy video indexing systems
Improved accuracy and efficiency in identifying specific events within extensive video recordings.
Accelerated development of AI agents capable of autonomous analysis and action based on long-form visual data.
New paradigms for human-computer interaction with video, moving beyond traditional playback to intelligent query and synthesis.
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