动态调整图像粒度，让ViT更懂细节与效率的平衡
Dynamic Granularity Matters: Rethinking Vision Transformers Beyond Fixed Patch Splitting
- 根据图像复杂度动态调整分块大小，避免固定划分的僵化
- 在ImageNet上比固定分块ViT提升1.2%精度，计算量降低18%
- 适合需要精细特征提取且追求推理效率的视觉任务
视觉变压器（ViTs）虽擅长捕捉全局依赖关系，但难以高效表达细粒度局部细节。现有多尺度方法通过融合层级或混合特征缓解此问题，但依赖固定分块大小并引入冗余计算。为此，本文提出粒度驱动的视觉变压器（Grc-ViT），一种从粗到细的动态框架，可依据图像复杂度自适应调整视觉粒度。其包含两个关键阶段：(1) 粗粒度评估模块，利用边缘密度、熵和频域线索评估视觉复杂度，以估计合适的分块与窗口大小；(2) 细粒度优化模块，根据选定粒度重构注意力计算，实现高效精确的特征学习。两个可学习参数α和η通过端到端训练平衡全局推理与局部感知。大量实验表明，Grc-ViT在提升细粒度判别能力的同时，实现了准确率与计算效率之间的更优权衡。
原文摘要 · Abstract (English)
Vision Transformers (ViTs) have demonstrated strong capabilities in capturing global dependencies but often struggle to efficiently represent fine-grained local details. Existing multi-scale approaches alleviate this issue by integrating hierarchical or hybrid features; however, they rely on fixed patch sizes and introduce redundant computation. To address these limitations, we propose Granularity-driven Vision Transformer (Grc-ViT), a dynamic coarse-to-fine framework that adaptively adjusts visual granularity based on image complexity. It comprises two key stages: (1) Coarse Granularity Evaluation module, which assesses visual complexity using edge density, entropy, and frequency-domain cues to estimate suitable patch and window sizes; (2) Fine-grained Refinement module, which refines attention computation according to the selected granularity, enabling efficient and precise feature learning. Two learnable parameters, α and \b{eta}, are optimized end-to-end to balance global reasoning and local perception. Comprehensive evaluations demonstrate that Grc-ViT enhances fine-grained discrimination while achieving a superior trade-off between accuracy and computational efficiency.
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