FastCAR统一处理物体分类与硬度回归，提升多任务学习效率。
FastCAR: Fast Classification And Regression for Task Consolidation in Multi-Task Learning to Model a Continuous Property Variable of Detected Object Class
- 通过标签转换实现单网络架构的分类与回归联合建模
- 分类准确率99.54%，回归误差2.4%，优于传统多任务模型
- 训练快2.52倍，推理快55%，适合工业实时检测场景
FastCAR是一种新型多任务学习中的任务融合方法，用于解决物体分类与连续属性回归之间的异构性挑战。该方法针对占据图像全帧的物体分类任务和同一物类实例的连续属性（如硬度）回归任务，提出一种适配单任务回归网络架构的标签变换策略。在自研的"Advanced Steel Property Dataset"上验证，该数据集包含4536张224x224像素图像，标注了离散类别与可取连续值的硬度属性。实验表明，FastCAR在联合学习两个任务时，分类准确率达99.54%，回归平均绝对百分比误差为2.4%，显著优于多种主流多任务模型。相比基准多任务网络，其训练速度提升2.52倍，推理延迟降低55%。
原文摘要 · Abstract (English)
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.
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