论文标题
地面真相或DAER：选择性重新查询次要信息
Ground-truth or DAER: Selective Re-query of Secondary Information
论文作者
论文摘要
许多视觉任务在推理时使用次要信息（种子）来协助计算机视觉模型解决问题。例如，需要一个初始边界框来初始化视觉对象跟踪。迄今为止，所有这些工作都假设种子是一个好种子。但是，实际上，从众包到嘈杂的自动种子，通常情况并非如此。因此，我们提出了种子排斥的问题 - 确定是否根据预期的性能降解来拒绝种子，而不是种子代替金标准种子。我们为此问题提供了正式的定义，并专注于两个有意义的子目标：了解错误的原因，并了解模型对基本输入条件的嘈杂种子的反应。考虑到这些目标，我们提出了一种新颖的培训方法和针对种子拒绝问题的评估指标。然后，我们使用观点估计的种子版本和细粒度分类任务来评估这些贡献。在这些实验中，我们表明我们的方法可以将目标性能审查的种子数量减少超过23％，而与强质基础相比。
Many vision tasks use secondary information at inference time -- a seed -- to assist a computer vision model in solving a problem. For example, an initial bounding box is needed to initialize visual object tracking. To date, all such work makes the assumption that the seed is a good one. However, in practice, from crowdsourcing to noisy automated seeds, this is often not the case. We hence propose the problem of seed rejection -- determining whether to reject a seed based on the expected performance degradation when it is provided in place of a gold-standard seed. We provide a formal definition to this problem, and focus on two meaningful subgoals: understanding causes of error and understanding the model's response to noisy seeds conditioned on the primary input. With these goals in mind, we propose a novel training method and evaluation metrics for the seed rejection problem. We then use seeded versions of the viewpoint estimation and fine-grained classification tasks to evaluate these contributions. In these experiments, we show our method can reduce the number of seeds that need to be reviewed for a target performance by over 23% compared to strong baselines.
