Abstract
Many vision tasks require side information at inference time---a seed---to fully specify the problem. For example, an initial object segmentation is needed for video object segmentation. To date, all such work makes the tacit assumption that the seed is a good one. However, in practice, from crowd-sourcing to noisy automated seeds, this is not the case. We hence propose the novel problem of seed rejection---determining whether to reject a seed based on expected degradation relative to the gold-standard. We provide a formal definition to this problem, and focus on two challenges: distinguishing poor primary inputs from poor seeds and understanding the model's response to noisy seeds conditioned on the primary input. With these challenges in mind, we propose a novel training method and evaluation metrics for the seed rejection problem. We then validate these metrics and methods on two problems which use seeds as a source of additional information: keypoint-conditioned viewpoint estimation with crowdsourced seeds and hierarchical scene classification with automated seeds. In these experiments, we show our method reduces the required number of seeds that need to be reviewed for a target performance by up to 23% over strong baselines.
Abstract (translated)
URL
https://arxiv.org/abs/2009.07414
