ViT在高分辨率场景下计算量爆炸的问题终于有了优雅解法——VECA用线性复杂度实现竞争性能，做视觉模型部署或高分辨率图像处理的团队值得关注。
VECA（Visual Elastic Core Attention）提出了一种新的视觉Transformer架构，用核心-外围结构替代传统的全对全自注意力。通过一组可学习的核心token作为通信接口，图像patch只与核心交互，计算复杂度从O(N²)降为O(N)。该模型在分类和密集预测任务上性能与最新视觉基础模型相当，同时大幅降低计算成本。VECA还支持在推理时弹性调整计算量与精度，为高分辨率视觉任务提供了可扩展的替代方案。
Elastic Attention Cores for Scalable Vision Transformers
Vision Transformers (ViTs) achieve strong data-driven scaling by leveraging all-to-all self-attention. However, this flexibility incurs a computational cost that scales quadratically with image resolution, limiting ViTs in high-resolution domains. Underlying this approach is the assumption that pairwise token interactions are necessary for learning rich visual-semantic representations. In this work, we challenge this assumption, demonstrating that effective visual representations can be learned without any direct patch-to-patch interaction. We propose VECA (Visual Elastic Core Attention), a vision transformer architecture that uses efficient linear-time core-periphery structured attention enabled by a small set of learned cores. In VECA, these cores act as a communication interface: patch tokens exchange information exclusively through the core tokens, which are initialized from scratch and propagated across layers. Because the $N$ image patches only directly interact with a resolution invariant set of $C$ learned "core" embeddings, this yields linear complexity $O(N)$ for predetermined $C$, which bypasses quadratic scaling. Compared to prior cross-attention architectures, VECA maintains and iteratively updates the full set of $N$ input tokens, avoiding a small $C$-way bottleneck. Combined with nested training along the core axis, our model can elastically trade off compute and accuracy during inference. Across classification and dense tasks, VECA achieves performance competitive with the latest vision foundation models while reducing computational cost. Our results establish elastic core-periphery attention as a scalable alternative building block for Vision Transformers.