论文标题
本地计数模型的离散回归
Discrete-Constrained Regression for Local Counting Models
论文作者
论文摘要
本质上，本地计数或局部区域中的对象数量本质上是连续的价值。然而，最近的最新方法表明，将计数为分类任务的制定性能优于回归。通过一系列对经过精心控制的合成数据的实验，我们表明，这种反直觉结果是由不精确的地面真相局部计数引起的。诸如偏见的点注释和用于产生地面真实计数的高斯内核的因素引起了与真实的局部计数的偏差。标准连续回归对这些错误高度敏感，从而解释了分类和回归之间的性能差距。为了减轻灵敏度，我们将回归公式从连续规模放松为离散的排序，并提出了一种新型的离散约束（DC）回归。应用于人群计数，DC回归比三个公共基准的分类和标准回归更为准确。对于年龄估计任务也有类似的优势，从而验证了DC回归的总体有效性。
Local counts, or the number of objects in a local area, is a continuous value by nature. Yet recent state-of-the-art methods show that formulating counting as a classification task performs better than regression. Through a series of experiments on carefully controlled synthetic data, we show that this counter-intuitive result is caused by imprecise ground truth local counts. Factors such as biased dot annotations and incorrectly matched Gaussian kernels used to generate ground truth counts introduce deviations from the true local counts. Standard continuous regression is highly sensitive to these errors, explaining the performance gap between classification and regression. To mitigate the sensitivity, we loosen the regression formulation from a continuous scale to a discrete ordering and propose a novel discrete-constrained (DC) regression. Applied to crowd counting, DC-regression is more accurate than both classification and standard regression on three public benchmarks. A similar advantage also holds for the age estimation task, verifying the overall effectiveness of DC-regression.
