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- Paper link: https://arxiv.org/pdf/2405.17871
- Code link: https://github.com/foundation-multimodal-models/CAL
- can be directly nested into the training process without additional pre-training stage.
- has achieved significant improvements in OCR and Caption benchmarks. From the visualization, it can be found that CAL makes the image modal alignment better.
- CAL makes the training process more resistant to noisy data.
Text that is highly related to pictures: such as entities ( Such as people, animals, objects), quantity, color, text, etc. These tokens directly correspond to image information and are crucial for multi-modal alignment. Text with low correlation to the picture: Such as following words or content that can be inferred from the previous text. These tokens are actually mainly used to train the plain text capabilities of VLM. Text that contradicts the image content: These tokens are inconsistent with image information and may even provide misleading information, negatively affecting the multi-modal alignment process.
标 Figure 1: The green mark is related to the high -related Token, the red is the contrary to the content, and the colorless is the neutral Token
- If you add image input in front, it is equivalent to providing additional contextual information. In this case, the logit of each text token will be adjusted based on the new situation. The logit changes in these two cases represent the impact of the new condition of the picture on each text token.
- Specifically, during the training process, CAL inputs the image and text sequences and individual text sequences into the large language model (LLM) respectively to obtain the logit of each text token. By calculating the logit difference between these two cases, we can measure the impact of the image on each token. The larger the logit difference, the greater the impact of the image on the token, so the token is more relevant to the image. The figure below shows the flow chart of the logit diff and CAL methods for text tokens.对 Figure 2: The left picture is the visualization of the token logit diff in the two situations. The picture on the right is the visualization of the CAL method process


The above is the detailed content of Bytedance Doubao and Wuhan University proposed CAL: enhancing multi-modal alignment effects through visually related tokens. For more information, please follow other related articles on the PHP Chinese website!

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