标题： FastCAR：多任务学习中用于检测对象类别连续属性变量建模的任务巩固的快速分类与回归 显示英文标题

标题： FastCAR: Fast Classification And Regression for Task Consolidation in Multi-Task Learning to Model a Continuous Property Variable of Detected Object Class

摘要： FastCAR 是一种新颖的任务聚合方法，适用于多任务学习（MTL），用于分类和回归任务，尽管任务异质性并非微不足道且仅有细微的相关性。 该方法解决了检测到的对象分类（占据整个图像帧）以及连续属性变量的回归建模（针对某一对象类别的实例），这是科学和工程中的一个关键应用场景。 FastCAR 包含一种标签转换方法，可与单一任务回归网络架构配合使用。 当同时考虑学习这两个任务时，FastCAR 在传统 MTL 模型族（基于架构和损失加权方案的参数化）的表现上更为出色（分类准确率为 99.54%，回归平均绝对百分比误差为 2.4%）。 所进行的实验采用了我们贡献的“高级钢材属性数据集”https://github.com/fastcandr/AdvancedSteel-Property-Dataset。 该数据集包含 4536 张 224x224 像素的图像，标注了离散的对象类别及其硬度属性，后者可以取连续值。 我们提出的 FastCAR 方法在任务聚合方面实现了训练时间效率（快 2.52 倍）和降低推理延迟（快 55%）优于基准 MTL 网络。 显示英文摘要

摘要： FastCAR is a novel task consolidation approach in Multi-Task Learning (MTL) for a classification and a regression task, despite the non-triviality of task heterogeneity with only a subtle correlation. The approach addresses the classification of a detected object (occupying the entire image frame) and regression for modeling a continuous property variable (for instances of an object class), a crucial use case in science and engineering. FastCAR involves a label transformation approach that is amenable for use with only a single-task regression network architecture. FastCAR outperforms traditional MTL model families, parametrized in the landscape of architecture and loss weighting schemes, when learning both tasks are collectively considered (classification accuracy of 99.54%, regression mean absolute percentage error of 2.4%). The experiments performed used "Advanced Steel Property Dataset" contributed by us https://github.com/fastcandr/AdvancedSteel-Property-Dataset. The dataset comprises 4536 images of 224x224 pixels, annotated with discrete object classes and its hardness property that can take continuous values. Our proposed FastCAR approach for task consolidation achieves training time efficiency (2.52x quicker) and reduced inference latency (55% faster) than benchmark MTL networks.