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English(EN) TinyFlow: Breaking the Warping Bottleneck for Edge Optical Flow

TinyFlow网络突破边缘光流的扭曲瓶颈

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了TinyFlow，一种专为资源受限的边缘设备设计的新型光流网络。与依赖于依赖光流的扭曲的传统网络不同，TinyFlow采用无扭曲方法，使用固定窗口相关性。这种设计允许整个网络编译为单个静态INT8图，显著提高了效率。TinyFlow在MPI Sintel和FlyingChairs等各种基准测试中，在参数更少的情况下，表现优于EdgeFlowNet和NanoFlowNet等现有模型。其在TartanAir和UZH-FPV数据集上的单目视觉里程计应用也取得了具有竞争力的轨迹误差率。 AI

影响 能够更有效地在边缘设备上部署光流模型，有可能提高机器人和自主系统中的实时感知能力。

排序理由 该集群包含一篇详细介绍新模型及其性能的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

TinyFlow网络突破边缘光流的扭曲瓶颈

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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Xiao'ao Song, Chahat Deep Singh ·

    TinyFlow：打破边缘光流的扭曲瓶颈

    arXiv:2610.33338v1 Announce Type: cross Abstract: High-performance optical flow networks are difficult to deploy on resource-constrained edge accelerators because their iterative refinement warps features by the current flow, which relies on flow-dependent resampling and data-dep…

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