Language-Pretraining-Induced Bias: A Strong Foundation for General Vision Tasks
The ratio of outlier parameters in language pre-training models and vision pre-training models differs significantly, making cross-modality (language and vision) inherently more challenging than cross-domain adaptation. As a result, many prior studies have focused on cross-domain transfer rather than attempting to bridge language and vision modalities, assuming that language pre-trained models are unsuitable for downstream visual tasks due to disparate parameter spaces. Contrary to this assumption, we show that adding a bridge training stage as a modality adaptation learner can effectively align Large Language Model (LLM) parameters with vision tasks. Specifically, we propose a simple yet...
| Authors | Yaxin Luo, Zhiqiang Shen |
|---|---|
| Primary category | cs.CV |
| Published | 2 April 2026 |
| Last updated at source | 3 April 2026 |
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