Previous work shows that humans tend to prefer large bounding boxes over small bounding boxes with the same IoU. However, we show here that commonly used object detectors predict large and small boxes equally often. In this work, we investigate how to align automatically detected object boxes with human preference and study whether this improves human quality perception. We evaluate the performance of three commonly used object detectors through a user study (N = 123). We find that humans prefer object detections that are upscaled with factors of 1.5 or 2, even if the corresponding AP is close to 0. Motivated by this result, we propose an asymmetric bounding box regression loss that encourages large over small predicted bounding boxes. Our evaluation study shows that object detectors fine-tuned with the asymmetric loss are better aligned with human preference and are preferred over fixed scaling factors. A qualitative evaluation shows that human preference might be influenced by some object characteristics, like object shape.

翻译：先前的研究表明，在IoU相同的情况下，人类倾向于偏好大尺寸边界框而非小尺寸边界框。然而，本文指出，常用目标检测器预测大框与小框的频率基本相当。本工作研究如何使自动检测的目标框与人类偏好对齐，并探讨这种对齐是否能提升人类感知质量。我们通过一项用户研究（N = 123）评估了三种常用目标检测器的性能。研究发现，即使对应AP接近0，人类仍偏好按1.5或2倍系数放大的检测框。基于此发现，我们提出一种非对称边界框回归损失函数，该函数促使模型更倾向于预测大尺寸边界框。评估研究表明，使用非对称损失微调的目标检测器能更好地与人类偏好对齐，且其效果优于固定缩放因子。定性分析表明，人类偏好可能受物体形状等特征影响。