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English(EN) The frame-level leakage trap: rethinking evaluation protocols for intrinsic image decomposition, with source-separable uncertainty as a case study

新研究揭示图像分解评估缺陷,提出场景级划分

一篇新论文指出了内在图像分解模型评估中的一个重大缺陷,特别是在MPI Sintel数据集上。研究人员发现,按帧而非按场景划分数据集,由于训练帧和测试帧之间的空间相似性,会导致性能指标虚高高达2.0 dB。他们提议将场景级划分作为标准,并展示了一种具有源可分离不确定性的新型物理信息分解方法,该方法通过过滤不确定像素来改进下游任务。 AI

影响 强调了计算机视觉领域需要更严格的评估协议,这可能会影响未来分解模型的基准测试和开发方式。

排序理由 该集群包含一篇学术论文,详细介绍了新的评估协议和一种新颖的内在图像分解方法。

在 arXiv cs.CV 阅读 →

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新研究揭示图像分解评估缺陷,提出场景级划分

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该集群包含一篇学术论文,详细介绍了新的评估协议和一种新颖的内在图像分解方法。
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报道来源 [2]

  1. arXiv cs.CV TIER_1 English(EN) · Jihwan Woo ·

    逐帧泄露陷阱:重新思考内禀图像分解的评估协议,以源可分离不确定性为例

    arXiv:2605.06359v1 Announce Type: cross Abstract: Evaluation protocols for learned intrinsic image decomposition on MPI Sintel have been inconsistent. Several prior works split the dataset by frames, which allows spatially similar frames of the same scene to appear in both train …

  2. arXiv cs.CV TIER_1 English(EN) · Jihwan Woo ·

    帧级泄露陷阱:重新思考内禀图像分解的评估协议,以源可分离不确定性为例

    Evaluation protocols for learned intrinsic image decomposition on MPI Sintel have been inconsistent. Several prior works split the dataset by frames, which allows spatially similar frames of the same scene to appear in both train and test partitions. We quantify this leakage effe…