Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and
  Language Models
Conversation agents fueled by Large Language Models (LLMs) are providing a
new way to interact with visual data. While there have been initial attempts
for image-based conversation models, this work addresses the underexplored
field of video-based conversation by introducing Video-ChatGPT. It is a
multimodal model that merges a video-adapted visual encoder with a LLM. The
model is capable of understanding and generating human-like conversations about
videos. We introduce a new dataset of 100,000 video-instruction pairs used to
train Video-ChatGPT acquired via manual and semi-automated pipeline that is
easily scalable and robust to label noise. We also develop a quantiative
evaluation framework for video-based dialogue models to objectively analyse the
strengths and weaknesses of proposed models. Our code, models, instruction-sets
and demo are released at https://github.com/mbzuai-oryx/Video-ChatGPT.
64828c62e4bbb1c2dd322d1e
Muhammad Maaz
claimed_verified
2023-06-12T09:12:33.798Z
64807585856901b0edb8d68b
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/kmCo6brXhQ7SyxVx3lpsx.jpeg
Muhammad Maaz
user
mmaaz60
64828c62e4bbb1c2dd322d1f
Hanoona Rasheed
admin_assigned
2023-06-09T16:39:27.933Z
64636b2551fa6e6306046293
https://cdn-avatars.huggingface.co/v1/production/uploads/64636b2551fa6e6306046293/Uuz6z2MZb_LKLGM8uxF9s.jpeg
Hanoona Rasheed
user
Hanoona
64828c62e4bbb1c2dd322d20
Salman Khan
64828c62e4bbb1c2dd322d21
Fahad Shahbaz Khan
Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and
  Language Models
Conversation agents fueled by Large Language Models (LLMs) are providing a
new way to interact with visual data. While there have been initial attempts
for image-based conversation models, this work addresses the underexplored
field of video-based conversation by introducing Video-ChatGPT. It is a
multimodal model that merges a video-adapted visual encoder with a LLM. The
model is capable of understanding and generating human-like conversations about
videos. We introduce a new dataset of 100,000 video-instruction pairs used to
train Video-ChatGPT acquired via manual and semi-automated pipeline that is
easily scalable and robust to label noise. We also develop a quantiative
evaluation framework for video-based dialogue models to objectively analyse the
strengths and weaknesses of proposed models. Our code, models, instruction-sets
and demo are released at https://github.com/mbzuai-oryx/Video-ChatGPT.
Video-ChatGPT is a multimodal model combining a video-adapted visual encoder with a Large Language Model to understand and generate human-like conversations about videos, trained on a scalable dataset of video-instruction pairs.
Large Language Models multimodal model video-adapted visual encoder human-like conversations video-based conversation video-instruction pairs manual and semi-automated pipeline quantiative evaluation framework video-based dialogue models