提出KCLA机制，在线性复杂度下实现动态跨层交互。
Enhancing Layer Interaction Using Key-Correlated Layer Attention
- 基于键向量高余弦相似性，设计动态关联的层间注意力
- 计算复杂度线性增长，比传统方法快数倍且保持长程依赖
- 适合深度网络，尤其图像识别与医学图像分割任务
近期网络架构设计引入层注意力以增强层间交互。此类框架中，每层查询所有前序层建立跨层连接，但导致与网络深度呈二次方增长的计算复杂度。为缓解此问题，已有研究提出循环层注意力（RLA）和线性注意力机制，但存在信息更新静态、长距离依赖建模能力有限等缺陷。本文提出关键相关层注意力（KCLA），受观察到的层注意力中键表示具有高余弦相似性的启发。KCLA在保持动态信息更新的同时实现线性计算复杂度，直接源于层注意力的基础定义。此外，其具备固定的内存空间复杂度，不随网络深度变化。实验表明，KCLA在图像识别、目标检测和医学图像分割等多种任务上均表现优异。代码已开源：https://github.com/bgx666/KCLA。
原文摘要 · Abstract (English)
Recent advances in network architecture design have introduced layer attention to enhance inter-layer interactions. In such frameworks, each layer queries all preceding layers to establish cross-layer connections. However, layer attention results in quadratic computational complexity with respect to network depth. To mitigate this issue, prior works have proposed Recurrent Layer Attention (RLA) and linear attention mechanisms, which suffer from static information updates and limited long-range cross-layer dependency modeling. To overcome these limitations, we propose Key-Correlated Layer Attention (KCLA), inspired by our observation that Key representations in layer attention exhibit high cosine similarity. KCLA achieves linear computational complexity while preserving dynamic information updates, directly derived from the foundational definition of layer attention. Furthermore, KCLA maintains long-range cross-layer connections and features a fixed spatial complexity, independent of network depth. Empirical evaluations demonstrate that KCLA delivers good performance across diverse tasks, including image recognition, object detection, and medical image segmentation. The code is publicly available at https://github.com/bgx666/KCLA.
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