WeMMU：通过噪声查询令牌增强视觉语言模型与扩散模型的桥接
WeMMU: Enhanced Bridging of Vision-Language Models and Diffusion Models via Noisy Query Tokens
用带噪声的可学习查询令牌作为 VLM 与扩散模型之间的分布式接口，并配一条 VAE 线性投影支路补回细节，可直接用于构建视觉生成式多模态助手，缓解固定查询令牌在跨任务时的泛化崩溃。
原文依据 we propose Noisy Query Tokens, which learn a distributed representation space between the VLM and Diffusion Model via end-to-end optimization
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论多模态大语言模型（MLLM）的最新进展凸显了高效桥接预训练视觉语言模型（VLM）与扩散模型的挑战。使用固定数量可学习查询令牌的方法虽然具有计算效率，但会出现任务泛化崩溃，无法适应与其预训练任务相距较远的新任务。为克服这一问题，我们提出噪声查询令牌，通过端到端优化在 VLM 与扩散模型之间学习一个分布式表示空间，从而增强持续学习。此外，我们引入带有线性投影的变分自编码器分支，以恢复细粒度的图像细节。实验结果证实，我们的方法缓解了泛化崩溃，并能在多样任务上实现稳定的持续学习。
英文原文摘要 825 字符
Recent progress in multimodal large language models (MLLMs) has highlighted the challenge of efficiently bridging pre-trained Vision-Language Models (VLMs) with Diffusion Models. While methods using a fixed number of learnable query tokens offer computational efficiency, they suffer from task generalization collapse, failing to adapt to new tasks that are distant from their pre-training tasks. To overcome this, we propose Noisy Query Tokens, which learn a distributed representation space between the VLM and Diffusion Model via end-to-end optimization, enhancing continual learning. Additionally, we introduce a VAE branch with linear projection to recover fine-grained image details. Experimental results confirm our approach mitigates generalization collapse and enables stable continual learning across diverse tasks.