VISTA: Enhancing Long-Duration and High-Resolution Video Understanding
  by Video Spatiotemporal Augmentation
Authors: name: Weiming Ren, name: Huan Yang, name: Jie Min, name: Cong Wei, name: Wenhu Chen

Current large multimodal models (LMMs) face significant challenges in
processing and comprehending long-duration or high-resolution videos, which is
mainly due to the lack of high-quality datasets. To address this issue from a
data-centric perspective, we propose VISTA, a simple yet effective Video
Spatiotemporal Augmentation framework that synthesizes long-duration and
high-resolution video instruction-following pairs from existing video-caption
datasets. VISTA spatially and temporally combines videos to create new
synthetic videos with extended durations and enhanced resolutions, and
subsequently produces question-answer pairs pertaining to these newly
synthesized videos. Based on this paradigm, we develop seven video augmentation
methods and curate VISTA-400K, a video instruction-following dataset aimed at
enhancing long-duration and high-resolution video understanding. Finetuning
various video LMMs on our data resulted in an average improvement of 3.3%
across four challenging benchmarks for long-video understanding. Furthermore,
we introduce the first comprehensive high-resolution video understanding
benchmark HRVideoBench, on which our finetuned models achieve a 6.5%
performance gain. These results highlight the effectiveness of our framework.