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Multimodal

ImageBind-LLM

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First page
ImageBind-LLM
The curator’s take

Shanghai AI Lab's ImageBind-LLM brings six-modality understanding to LLMs via the ImageBind joint embedding space.

Key points
01

ImageBind backbone: Leverages ImageBind's joint embedding space (covering image, text, audio, depth, thermal, IMU) as a universal multimodal encoder.

02

Learnable bind network: Aligns ImageBind's visual encoder with a frozen LLM through a learnable bind network, enabling instruction tuning across modalities.

03

Six-modality input: Responds to instructions over audio, 3D point clouds, video, and beyond - not just text and image.

04

Generation quality: Maintains high language-generation quality despite the modality diversity, validating the ImageBind-as-bridge approach.

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