← Tuesday, October 6, 2026InferencePaper
LoHi improves long-video VLM accuracy by balancing frame resolution and coverage
LoHi combines dense low-resolution video with sparse high-resolution frames, improving average accuracy by 10.6 percentage points across three benchmarks; the study also finds front-end decoding dominates hour-long video wall time. This offers concrete guidance for trading visual detail against temporal coverage and latency.
- What
- LoHi is a training-free, single-pass framework for long-video VLMs that combines dense low-resolution video with sparse high-resolution frames.
- Method
- Dense low-resolution sampling beats sparse native-resolution sampling at matched token budgets, while front-end decoding dominates hour-long video wall time.
- Why it matters
- LoHi improves average accuracy by 10.6 percentage points over the native-resolution baseline at a matched token budget and cuts decoding latency by up to 7× on hour-long videos.
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