Unleashing Hour-Scale Video Training for Long Video-Language
  Understanding
Authors: name: Jingyang Lin, name: Jialian Wu, name: Ximeng Sun, name: Ze Wang, name: Jiang Liu, name: Yusheng Su, name: Xiaodong Yu, name: Hao Chen, name: Jiebo Luo, name: Zicheng Liu, name: Emad Barsoum

Recent long-form video-language understanding benchmarks have driven progress
in video large multimodal models (Video-LMMs). However, the scarcity of
well-annotated long videos has left the training of hour-long Video-LLMs
underexplored. To close this gap, we present VideoMarathon, a large-scale
hour-long video instruction-following dataset. This dataset includes around
9,700 hours of long videos sourced from diverse domains, ranging from 3 to 60
minutes per video. Specifically, it contains 3.3M high-quality QA pairs,
spanning six fundamental topics: temporality, spatiality, object, action,
scene, and event. Compared to existing video instruction datasets,
VideoMarathon significantly extends training video durations up to 1 hour, and
supports 22 diverse tasks requiring both short- and long-term video
comprehension. Building on VideoMarathon, we propose Hour-LLaVA, a powerful and
efficient Video-LMM for hour-scale video-language modeling. It enables
hour-long video training and inference at 1-FPS sampling by leveraging a memory
augmentation module, which adaptively integrates user question-relevant and
spatiotemporal-informative semantics from a cached full video context. In our
experiments, Hour-LLaVA achieves the best performance on multiple long
video-language benchmarks, demonstrating the high quality of the VideoMarathon
dataset and the superiority of the Hour-LLaVA model.