ImageBind-LLM is a multi-modality instruction tuning method for large language models. It can respond to audio, 3D point clouds, video, and their embedding-space arithmetic using only image-text alignment training. The model uses a learnable bind network to align the embedding space between LLaMA and ImageBind's image encoder, allowing visual instructions to be injected via a gating mechanism. During inference, a visual cache model enhances cross-modal embedding. The approach enables the model to generate high-quality language and respond to diverse modalities.

https://arxiv.org/abs//2309.03905

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