MovieLLM: Enhancing Long Video Understanding with AI-Generated Movies
The development of multimodal models has marked a significant step forward in
how machines understand videos. These models have shown promise in analyzing
short video clips. However, when it comes to longer formats like movies, they
often fall short. The main hurdles are the lack of high-quality, diverse video
data and the intensive work required to collect or annotate such data. In the
face of these challenges, we propose MovieLLM, a novel framework designed to
create synthetic, high-quality data for long videos. This framework leverages
the power of GPT-4 and text-to-image models to generate detailed scripts and
corresponding visuals. Our approach stands out for its flexibility and
scalability, making it a superior alternative to traditional data collection
methods. Our extensive experiments validate that the data produced by MovieLLM
significantly improves the performance of multimodal models in understanding
complex video narratives, overcoming the limitations of existing datasets
regarding scarcity and bias.
65e7482deea9f3f4c447113a
Zhende Song
65e7482deea9f3f4c447113b
Chenchen Wang
admin_assigned
2024-03-05T20:41:19.684Z
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chenchen
user
wangchenchen
65e7482deea9f3f4c447113c
Jiamu Sheng
admin_assigned
2024-03-05T20:41:07.264Z
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Jiamu Sheng
user
Yooaena
65e7482deea9f3f4c447113d
Chi Zhang
claimed_verified
2024-08-08T07:43:06.501Z
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Chi Zhang
user
DrChiZhang
65e7482deea9f3f4c447113e
Gang Yu
claimed_verified
2024-03-07T09:15:31.796Z
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Gang Yu
user
skicy
65e7482deea9f3f4c447113f
Jiayuan Fan
65e7482deea9f3f4c4471140
Tao Chen
MovieLLM: Enhancing Long Video Understanding with AI-Generated Movies
The development of multimodal models has marked a significant step forward in
how machines understand videos. These models have shown promise in analyzing
short video clips. However, when it comes to longer formats like movies, they
often fall short. The main hurdles are the lack of high-quality, diverse video
data and the intensive work required to collect or annotate such data. In the
face of these challenges, we propose MovieLLM, a novel framework designed to
create synthetic, high-quality data for long videos. This framework leverages
the power of GPT-4 and text-to-image models to generate detailed scripts and
corresponding visuals. Our approach stands out for its flexibility and
scalability, making it a superior alternative to traditional data collection
methods. Our extensive experiments validate that the data produced by MovieLLM
significantly improves the performance of multimodal models in understanding
complex video narratives, overcoming the limitations of existing datasets
regarding scarcity and bias.
MovieLLM generates synthetic, high-quality data for long videos using GPT-4 and text-to-image models, improving multimodal models' performance on complex narratives.
GPT-4 text-to-image models multimodal models synthetic data complex video narratives