# Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding

**Authors:** Hang Zhang, Xin Li, Lidong Bing  
**arXiv:** [2306.02858](https://arxiv.org/abs/2306.02858)  
**Date:** 2023-06-05  
**Summary page:** https://www.summarizepaper.com/en/paper/2306.02858v4/

## Key points

- Video-LLaMA is a multi-modal framework for enhancing Large Language Models (LLMs) with visual and auditory understanding in videos.
- It introduces a Video Q-former that incorporates a pre-trained image encoder into the video encoder to capture temporal changes in visual scenes.
- Video-LLaMA utilizes ImageBind as the pre-trained audio encoder and introduces an Audio Q-former on top of it to learn reasonable auditory query embeddings.
- The framework is trained on video/image caption pairs and fine-tuned with higher quality visual instruction datasets.
- Results show that Video-LLaMA can perceive and comprehend video content, generating meaningful responses grounded in both visual and auditory information.
- It leverages shared embedding space provided by ImageBind to comprehend audio during inference, even without being trained on audio data.
- Related works include models like Vicuna and Baize for different NLP tasks, as well as approaches using LLMs as controllers or existing multi-modal models for training fundamental large-scale multi-modal models.
- Video - LLaMa builds upon these advancements by providing plug-and-play plugins for enabling LLMs to understand both visual and auditory content in videos.

## Summary

Video-LLaMA is a multi-modal framework that enhances the capabilities of Large Language Models (LLMs) by enabling them to understand both visual and auditory content in videos. To address the challenge of capturing temporal changes in visual scenes, Video-LLaMA introduces a Video Q-former that incorporates a pre-trained image encoder into the video encoder and learns video-language correspondence through a video-to-text generation task. For integrating audio-visual signals, Video-LLaMA utilizes ImageBind, a universal embedding model aligning multiple modalities, as the pre-trained audio encoder and introduces an Audio Q-former on top of ImageBind to learn reasonable auditory query embeddings for the LLM module. The framework is trained on massive video/image caption pairs and then fine tuned with visual instruction datasets of higher quality but moderate amount. Results show that Video-LLaMA exhibits the ability to perceive and comprehend video content, generating meaningful responses grounded in both visual and auditory information presented in videos. It also leverages shared embedding space provided by ImageBind to comprehend audio during inference even though it has not been trained on audio data. This demonstrates the versatility of Video-LLaMa in understanding multi modal inputs. In terms of related works, researchers have extended LLMs capabilities by developing models like Vicuna and Baize for different NLP tasks while multi modal LLMs have been explored using approaches falling into two categories: using LLMs as controllers or utilizing existing multi modal models as tools or training fundamental large scale multi modal models. Video - LLaMa builds upon these advancements by providing plug and play plugins that enable LLMs to comprehend both visual and auditory content in videos.