SlowFast-LLaVA: A Strong Training-Free Baseline for Video Large Language
  Models
Authors: name: Mingze Xu, name: Mingfei Gao, name: Zhe Gan, name: Hong-You Chen, name: Zhengfeng Lai, name: Haiming Gang, name: Kai Kang, name: Afshin Dehghan

We propose SlowFast-LLaVA (or SF-LLaVA for short), a training-free video
large language model (LLM) that can jointly capture the detailed spatial
semantics and long-range temporal context without exceeding the token budget of
commonly used LLMs. This is realized by using a two-stream SlowFast design of
inputs for Video LLMs to aggregate features from sampled video frames in an
effective way. Specifically, the Slow pathway extracts features at a low frame
rate while keeping as many spatial details as possible (e.g., with 24x24
tokens), and the Fast pathway operates on a high frame rate but uses a larger
spatial pooling stride (e.g., downsampling 6x) to focus on the motion cues. As
a result, this design allows us to adequately capture both spatial and temporal
features that are beneficial for understanding details along the video.
Experimental results show that SF-LLaVA outperforms existing training-free
methods on a wide range of video tasks. On some benchmarks, it achieves
comparable or even better performance compared to state-of-the-art Video LLMs
that are fine-tuned on video datasets.