SlowFast-LLaVA: A Strong Training-Free Baseline for Video Large Language Models
We propose SlowFast-LLaVA (or SF-LLaVA for short), a training-free video large language model (LLM) that can jointly capture 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 frames in an effective way. Specifically, the Slow pathway extracts features at a low frame rate while keeping as much spatial detail as possible (e.g., with 12x24 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 detailed video understanding. 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. Code has been made available at: https://github.com/apple/ml-slowfast-llava.
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Tasks
Language Modelling
Large Language Model
Video-based Generative Performance Benchmarking
Video-based Generative Performance Benchmarking (Consistency)
Video-based Generative Performance Benchmarking (Contextual Understanding)
Video-based Generative Performance Benchmarking (Correctness of Information)
Video-based Generative Performance Benchmarking (Detail Orientation))
Video-based Generative Performance Benchmarking (Temporal Understanding)
Video Understanding
Zero-Shot Video Question Answer
ActivityNet-QA
NExT-QA
TGIF-QA
