VideoLLaMA 2: Advancing Spatial-Temporal Modeling and Audio
  Understanding in Video-LLMs
Authors: name: Zesen Cheng; name: Sicong Leng; name: Hang Zhang; name: Yifei Xin; name: Xin Li; name: Guanzheng Chen; name: Yongxin Zhu; name: Wenqi Zhang; name: Ziyang Luo; name: Deli Zhao; name: Lidong Bing
Published: 2024-06-11 17:22:23

Abstract: In this paper, we present the VideoLLaMA 2, a set of Video Large Language
Models (Video-LLMs) designed to enhance spatial-temporal modeling and audio
understanding in video and audio-oriented tasks. Building upon its predecessor,
VideoLLaMA 2 incorporates a tailor-made Spatial-Temporal Convolution (STC)
connector, which effectively captures the intricate spatial and temporal
dynamics of video data. Additionally, we integrate an Audio Branch into the
model through joint training, thereby enriching the multimodal understanding
capabilities of the model by seamlessly incorporating audio cues. Comprehensive
evaluations on multiple-choice video question answering (MC-VQA), open-ended
video question answering (OE-VQA), and video captioning (VC) tasks demonstrate
that VideoLLaMA 2 consistently achieves competitive results among open-source
models and even gets close to some proprietary models on several benchmarks.
Furthermore, VideoLLaMA 2 exhibits reasonable improvements in audio-only and
audio-video question-answering (AQA & OE-AVQA) benchmarks over existing models.
These advancements underline VideoLLaMA 2's superior performance in multimodal
comprehension, setting a new standard for intelligent video analysis systems.
All models are public to facilitate further research.