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English(EN) SED:Lightweight Saliency prediction for Event-based data via Distillation

轻量级SED模型大幅缩小事件数据显著性预测模型尺寸

研究人员开发了SED,一种用于事件数据显著性预测的轻量级网络,通过知识蒸馏和新颖的深度可分离时空卷积块(DSTconv)显著减小了模型尺寸和参数数量。该方法将模型尺寸从180 MB大幅削减至0.32 MB,参数数量从4500万减少到81,000,同时在N-DHF1K和N-UCF Sports等基准数据集上保持或超越了性能。SED模型还展示了强大的泛化能力,能够成功地从合成事件数据迁移到其他模型失败的真实世界事件数据上。 AI

影响 这种轻量级模型可以通过降低事件感知计算需求,从而实现更高效的边缘AI应用。

排序理由 该集群包含一篇详细介绍新模型和方法的学术论文。

在 arXiv cs.CV 阅读 →

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轻量级SED模型大幅缩小事件数据显著性预测模型尺寸

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报道来源 [2]

  1. arXiv cs.CV TIER_1 English(EN) · Romaric Mazna, Jean Martinet, Michele Magno ·

    SED:通过蒸馏实现事件数据的轻量级显著性预测

    arXiv:2606.14631v1 Announce Type: new Abstract: Event-based saliency prediction has gained attention recently, as combining event cameras with saliency estimation can act as an upstream stage that naturally improves the efficiency of downstream eventbased perception at the edge. …

  2. arXiv cs.CV TIER_1 English(EN) · Michele Magno ·

    SED:通过蒸馏实现事件数据的轻量级显著性预测

    Event-based saliency prediction has gained attention recently, as combining event cameras with saliency estimation can act as an upstream stage that naturally improves the efficiency of downstream eventbased perception at the edge. However, current approaches are either neuromorp…