Predicting Multiple Structured Visual Interpretations

Debadeepta Dey, V. Ramakrishna, M. Hebert, J. Bagnell
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引用次数: 27

Abstract

We present a simple approach for producing a small number of structured visual outputs which have high recall, for a variety of tasks including monocular pose estimation and semantic scene segmentation. Current state-of-the-art approaches learn a single model and modify inference procedures to produce a small number of diverse predictions. We take the alternate route of modifying the learning procedure to directly optimize for good, high recall sequences of structured-output predictors. Our approach introduces no new parameters, naturally learns diverse predictions and is not tied to any specific structured learning or inference procedure. We leverage recent advances in the contextual submodular maximization literature to learn a sequence of predictors and empirically demonstrate the simplicity and performance of our approach on multiple challenging vision tasks including achieving state-of-the-art results on multiple predictions for monocular pose-estimation and image foreground/background segmentation.
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预测多重结构视觉解释
我们提出了一种简单的方法来产生少量具有高召回率的结构化视觉输出,用于各种任务,包括单目姿态估计和语义场景分割。目前最先进的方法学习单一模型并修改推理程序以产生少量不同的预测。我们采用修改学习过程的替代路线,直接优化结构化输出预测器的良好,高召回序列。我们的方法没有引入新的参数,自然地学习不同的预测,并且不依赖于任何特定的结构化学习或推理过程。我们利用上下文子模块最大化文献的最新进展来学习一系列预测因子,并通过经验证明我们的方法在多个具有挑战性的视觉任务上的简单性和性能,包括在单眼姿态估计和图像前景/背景分割的多个预测上取得最先进的结果。
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