提出轻量级事件到帧重建方法，兼顾精度与计算效率。
Computation-Aware Event-to-Frame Reconstruction via Selective Attention
- 用递归编码器逐步聚合事件信息，保持紧凑状态
- 在标准数据集上达到竞争性重建效果，计算开销低
- 适合实时视觉系统，尤其对快速运动和光照变化敏感场景
事件到帧（E2F）重建将异步事件流与基于帧的视觉流程衔接起来，但现有方法常面临重建质量与计算效率之间的权衡。本文提出一种高效E2F框架，强调因果时间建模与计算感知设计。架构采用递归编码器-解码器，以紧凑隐状态逐步聚合事件信息。为提升在快速运动和光照变化下的鲁棒性，引入选择性上下文融合策略，将事件驱动特征与先前强度线索结合。在此融合过程中，轻量级混合注意力机制增强特征选择性，避免依赖复杂的注意力操作。在标准基准上的实验结果表明，所提方法在保持良好准确率的同时，实现了精度与模型复杂度的合理平衡。
原文摘要 · Abstract (English)
Event-to-frame (E2F) reconstruction bridges asynchronous event streams with frame-based vision pipelines, but existing methods often face a trade-off between reconstruction quality and computational efficiency. In this work, we propose an efficient E2F framework that emphasizes causal temporal modeling and computation-aware design. The architecture adopts a recurrent encoder-decoder to incrementally aggregate event information with compact hidden states. To improve robustness under fast motion and illumination variations, a selective context fusion strategy is introduced to integrate event-driven features with prior intensity cues. Within this fusion process, a lightweight hybrid attention mechanism enhances feature selectivity without relying on heavy attention operations. Experimental results on standard benchmarks demonstrate that the proposed approach achieves competitive reconstruction performance while maintaining a favorable balance between accuracy and model complexity.
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