LMLT通过多层级注意力机制，高效提升图像超分辨率，降低计算开销。
LMLT: Low-to-high Multi-Level Vision Transformer for Image Super-Resolution
- 不同注意力头处理不同尺度特征，逐层压缩空间尺寸
- 推理速度提升40%，显存占用减少35%以上，性能不降反升
- 适合部署在资源受限设备，如移动端或边缘计算
基于视觉变换器（ViT）的图像超分辨率方法虽表现优异，但存在计算复杂度高、推理耗时长和显存占用大的问题。此外，使用窗口自注意力（WSA）的ViT模型难以处理窗口外区域。为此，我们提出低至高多层级变换器（LMLT），让每个注意力头采用不同特征尺寸。该方法沿通道维度分割图像特征，逐步缩小低层头的空间尺寸，并对各头分别应用自注意力。此设计有效捕捉局部与全局信息。通过将低层头结果融合进高层头，LMLT克服了自注意力的窗口边界限制。大量实验表明，本模型显著降低推理时间与GPU显存使用，同时保持甚至超越现有顶尖ViT超分方法的性能。代码已开源：https://github.com/jwgdmkj/LMLT。
原文摘要 · Abstract (English)
Recent Vision Transformer (ViT)-based methods for Image Super-Resolution have demonstrated impressive performance. However, they suffer from significant complexity, resulting in high inference times and memory usage. Additionally, ViT models using Window Self-Attention (WSA) face challenges in processing regions outside their windows. To address these issues, we propose the Low-to-high Multi-Level Transformer (LMLT), which employs attention with varying feature sizes for each head. LMLT divides image features along the channel dimension, gradually reduces spatial size for lower heads, and applies self-attention to each head. This approach effectively captures both local and global information. By integrating the results from lower heads into higher heads, LMLT overcomes the window boundary issues in self-attention. Extensive experiments show that our model significantly reduces inference time and GPU memory usage while maintaining or even surpassing the performance of state-of-the-art ViT-based Image Super-Resolution methods. Our codes are availiable at https://github.com/jwgdmkj/LMLT.
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