Comments (2)
感谢您对我们工作的关注!
您的理解是合理的。本文首先训练Image-to-LLM的projection module,并逐渐将其他模态的X-to-LLM projection也加入到相同的模块当中。 本质上是将一个Image-to-LLM模块进行微调,使其适应X-to-LLM alignment。
此处frozen image encoder作为一个通用的high-level semantic feature extractor,在一定程度上会限制其他模态的学习。可以简单的可以理解为其他模态与image的对齐。然而由于projection module会在多种数据上联合训练,其最终状态可能为一种折中状态,而非单纯的将其他模态对齐到image。
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get! 感谢讲解!
确实是很nice的思路
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Related Issues (20)
- About evaluation and training codes HOT 3
- Will the code for SFT be open sourced in the near future? Thanks~ HOT 1
- Fmri data is gray image or some numerical data HOT 1
- Is the Tonizer.py code converting modelities into tokens? HOT 2
- Training code HOT 4
- fmri data HOT 1
- webvid dataset no longer available as of 23 of Feb HOT 1
- freezing of LLM during pretrain stage HOT 2
- Clotho V2 annotation file HOT 5
- Inference inputs multiple modalities other than text at once HOT 2
- How to install petrel_client HOT 1
- Can't reproduce that Page 6, Table 5, Evaluation on Point Cloud-Text Tasks' Bleu, METEOR and ROUGE_L numbers HOT 2
- Whether the embedings generated by different modal data has comparability? HOT 2
- Vague output for audio HOT 3
- Distributed inference/demo checkpoints
- Model not producing accurate captions
- .
- Provide some inference examples HOT 1
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