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arxiv 2404.14687 v1 pith:G3T6RTLV submitted 2024-04-23 cs.MM cs.AIcs.CLcs.CV

Pegasus-v1 Technical Report

Raehyuk Jung , Hyojun Go , Jaehyuk Yi , Jiho Jang , Daniel Kim , Jay Suh , Aiden Lee , Cooper Han
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Jae Lee Jeff Kim Jin-Young Kim Junwan Kim Kyle Park Lucas Lee Mars Ha Minjoon Seo Abraham Jo Ed Park Hassan Kianinejad SJ Kim Tony Moon Wade Jeong Andrei Popescu Esther Kim EK Yoon Genie Heo Henry Choi Jenna Kang Kevin Han Noah Seo Sunny Nguyen Ryan Won Yeonhoo Park Anthony Giuliani Dave Chung Hans Yoon James Le Jenny Ahn June Lee Maninder Saini Meredith Sanders Soyoung Lee Sue Kim Travis Couture
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classification cs.MM cs.AIcs.CLcs.CV
keywords videopegasus-1reporttechnicalcontentlanguageacrossaddress
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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This technical report introduces Pegasus-1, a multimodal language model specialized in video content understanding and interaction through natural language. Pegasus-1 is designed to address the unique challenges posed by video data, such as interpreting spatiotemporal information, to offer nuanced video content comprehension across various lengths. This technical report overviews Pegasus-1's architecture, training strategies, and its performance in benchmarks on video conversation, zero-shot video question answering, and video summarization. We also explore qualitative characteristics of Pegasus-1 , demonstrating its capabilities as well as its limitations, in order to provide readers a balanced view of its current state and its future direction.

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Cited by 2 Pith papers

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  1. VideoLLaMA 3: Frontier Multimodal Foundation Models for Image and Video Understanding

    cs.CV 2025-01 unverdicted novelty 4.0

    VideoLLaMA3 uses a vision-centric training paradigm and token-reduction design to reach competitive results on image and video benchmarks.

  2. VideoLLaMA 2: Advancing Spatial-Temporal Modeling and Audio Understanding in Video-LLMs

    cs.CV 2024-06 unverdicted novelty 4.0

    VideoLLaMA 2 improves video LLMs via a new STC connector for spatial-temporal dynamics and joint audio training, reaching competitive results on video QA and captioning benchmarks.