title: Vript: A Video Is Worth Thousands of Words
summary: Advancements in multimodal learning, particularly in video understanding and
  generation, require high-quality video-text datasets for improved model
  performance. Vript addresses this issue with a meticulously annotated corpus of
  12K high-resolution videos, offering detailed, dense, and script-like captions
  for over 420K clips. Each clip has a caption of ~145 words, which is over 10x
  longer than most video-text datasets. Unlike captions only documenting static
  content in previous datasets, we enhance video captioning to video scripting by
  documenting not just the content, but also the camera operations, which include
  the shot types (medium shot, close-up, etc) and camera movements (panning,
  tilting, etc). By utilizing the Vript, we explore three training paradigms of
  aligning more text with the video modality rather than clip-caption pairs. This
  results in Vriptor, a top-performing video captioning model among open-source
  models, comparable to GPT-4V in performance. Vriptor is also a powerful model
  capable of end-to-end generation of dense and detailed captions for long
  videos. Moreover, we introduce Vript-Hard, a benchmark consisting of three
  video understanding tasks that are more challenging than existing benchmarks:
  Vript-HAL is the first benchmark evaluating action and object hallucinations in
  video LLMs, Vript-RR combines reasoning with retrieval resolving question
  ambiguity in long-video QAs, and Vript-ERO is a new task to evaluate the
  temporal understanding of events in long videos rather than actions in short
  videos in previous works. All code, models, and datasets are available in
  https://github.com/mutonix/Vript.
paper_authors._id: 6667ce1bf3e4efa2764a9f36
paper_authors.name: Dongjie Yang
paper_authors.status: claimed_verified
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paper_authors.fullname: Dongjie Yang
paper_authors.type: user
paper_authors.user: Mutonix
paper_authors._id: 6667ce1bf3e4efa2764a9f37
paper_authors.name: Suyuan Huang
paper_authors.status: admin_assigned
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paper_authors.fullname: Suyuan Huang
paper_authors.type: user
paper_authors.user: Florient
paper_authors._id: 6667ce1bf3e4efa2764a9f38
paper_authors.name: Chengqiang Lu
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paper_authors.fullname: lu
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paper_authors.user: cq
paper_authors._id: 6667ce1bf3e4efa2764a9f39
paper_authors.name: Xiaodong Han
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-11T07:59:10.593Z
paper_authors._id: 64638bddc615cbc12447f9f1
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paper_authors.fullname: Xiaodong Han
paper_authors.type: user
paper_authors.user: Hannnnnxd
paper_authors._id: 6667ce1bf3e4efa2764a9f3a
paper_authors.name: Haoxin Zhang
paper_authors._id: 6667ce1bf3e4efa2764a9f3b
paper_authors.name: Yan Gao
paper_authors._id: 6667ce1bf3e4efa2764a9f3c
paper_authors.name: Yao Hu
paper_authors._id: 6667ce1bf3e4efa2764a9f3d
paper_authors.name: Hai Zhao
paper_title: Vript: A Video Is Worth Thousands of Words
paper_summary: Advancements in multimodal learning, particularly in video understanding and
  generation, require high-quality video-text datasets for improved model
  performance. Vript addresses this issue with a meticulously annotated corpus of
  12K high-resolution videos, offering detailed, dense, and script-like captions
  for over 420K clips. Each clip has a caption of ~145 words, which is over 10x
  longer than most video-text datasets. Unlike captions only documenting static
  content in previous datasets, we enhance video captioning to video scripting by
  documenting not just the content, but also the camera operations, which include
  the shot types (medium shot, close-up, etc) and camera movements (panning,
  tilting, etc). By utilizing the Vript, we explore three training paradigms of
  aligning more text with the video modality rather than clip-caption pairs. This
  results in Vriptor, a top-performing video captioning model among open-source
  models, comparable to GPT-4V in performance. Vriptor is also a powerful model
  capable of end-to-end generation of dense and detailed captions for long
  videos. Moreover, we introduce Vript-Hard, a benchmark consisting of three
  video understanding tasks that are more challenging than existing benchmarks:
  Vript-HAL is the first benchmark evaluating action and object hallucinations in
  video LLMs, Vript-RR combines reasoning with retrieval resolving question
  ambiguity in long-video QAs, and Vript-ERO is a new task to evaluate the
  temporal understanding of events in long videos rather than actions in short
  videos in previous works. All code, models, and datasets are available in
  https://github.com/mutonix/Vript.
paper_ai_keywords: multimodal learning video understanding video generation high-quality video-text datasets denoising autoencoders video-text datasets captions script-like captions shot types camera movements training paradigms text-video alignment video captioning video scripting end-to-end generation dense captions detailed captions benchmark video LLM question answering (QA) temporal understanding event understanding action understanding Vripts-Hard Vript-HAL Vript-RR Vript-ERO Vript Dataset Vriptor Model VSM Model GPT-4V Vript Repository