用可演化原型对象替代固定图像块，实现更高效视觉注意力机制。
Neuroevolution of Self-Attention Over Proto-Objects
- 基于图像分割生成语义原型对象，替代传统矩形图像块作为注意力单元。
- 参数减少62%，训练时间缩短至2.6倍，性能达或超越当前最优水平。
- 适合追求轻量化、高效率视觉模型的研究者与工程师。
原型对象——具有共同视觉特性的图像区域——为神经网络中的注意力机制提供了比传统矩形图像块更优的替代方案。尽管以往工作证明在控制器网络协同下演化基于图像块的硬注意力模块可在视觉强化学习任务中达到顶尖性能，但本方法利用图像分割技术，直接作用于更高层级的特征。通过在原型对象而非固定图像块上操作，显著降低表示复杂度：每张图像分解为的原型对象数量远少于常规图像块，且每个原型对象可被高效编码为紧凑特征向量。这使得自注意力模块规模大幅缩减，同时处理更丰富的语义信息。实验表明，该原型对象方法在保持或超越基于图像块实现的最先进性能的同时，参数量减少62%，训练时间缩短至2.6倍。
原文摘要 · Abstract (English)
Proto-objects - image regions that share common visual properties - offer a promising alternative to traditional attention mechanisms based on rectangular-shaped image patches in neural networks. Although previous work demonstrated that evolving a patch-based hard-attention module alongside a controller network could achieve state-of-the-art performance in visual reinforcement learning tasks, our approach leverages image segmentation to work with higher-level features. By operating on proto-objects rather than fixed patches, we significantly reduce the representational complexity: each image decomposes into fewer proto-objects than regular patches, and each proto-object can be efficiently encoded as a compact feature vector. This enables a substantially smaller self-attention module that processes richer semantic information. Our experiments demonstrate that this proto-object-based approach matches or exceeds the state-of-the-art performance of patch-based implementations with 62% less parameters and 2.6 times less training time.
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