提出高效医学视频生成模型FEAT，解决动态一致性与噪声适应难题。
FEAT: Full-Dimensional Efficient Attention Transformer for Medical Video Generation
- 全维度注意力机制融合时空通道信息，增强全局依赖建模
- 线性复杂度设计使参数仅23%于当前最优模型，仍保持优异性能
- 残差值引导模块适配不同噪声水平，提升生成细节质量
生成高质量动态医学视频面临挑战，需兼顾空间一致性和时间动态性。现有基于Transformer的方法存在通道交互不足、自注意力计算复杂度高，以及时间步嵌入提供的去噪引导粗略等问题。本文提出FEAT——一种全维度高效注意力Transformer，通过三项创新解决上述问题：(1) 统一的时序-空间-通道注意力机制，捕捉多维全局依赖；(2) 每维线性复杂度注意力设计，采用加权键值注意力与全局通道注意力；(3) 残差值引导模块提供像素级精细指导，适应不同噪声水平。在标准基准和下游任务上评估表明，FEAT-S仅含当前最优模型Endora 23%参数，性能相当甚至更优；FEAT-L在多个数据集上超越所有对比方法，展现卓越效果与可扩展性。代码已开源。
原文摘要 · Abstract (English)
Synthesizing high-quality dynamic medical videos remains a significant challenge due to the need for modeling both spatial consistency and temporal dynamics. Existing Transformer-based approaches face critical limitations, including insufficient channel interactions, high computational complexity from self-attention, and coarse denoising guidance from timestep embeddings when handling varying noise levels. In this work, we propose FEAT, a full-dimensional efficient attention Transformer, which addresses these issues through three key innovations: (1) a unified paradigm with sequential spatial-temporal-channel attention mechanisms to capture global dependencies across all dimensions, (2) a linear-complexity design for attention mechanisms in each dimension, utilizing weighted key-value attention and global channel attention, and (3) a residual value guidance module that provides fine-grained pixel-level guidance to adapt to different noise levels. We evaluate FEAT on standard benchmarks and downstream tasks, demonstrating that FEAT-S, with only 23\% of the parameters of the state-of-the-art model Endora, achieves comparable or even superior performance. Furthermore, FEAT-L surpasses all comparison methods across multiple datasets, showcasing both superior effectiveness and scalability. Code is available at https://github.com/Yaziwel/FEAT.
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