SynMotor: A Benchmark Suite for Object Attribute Regression and Multi-task Learning
This work addresses the need for benchmarks in object attribute regression and multi-task learning in computer vision, particularly for remanufacturing applications, but it is incremental as it builds on existing benchmark concepts.
The paper introduces SynMotor, a benchmark suite with 2D synthetic images and 3D synthetic point clouds for small electric motors, providing annotations for detection, classification, segmentation, and multi-attribute regression tasks, along with evaluation metrics and baseline results.
In this paper, we develop a novel benchmark suite including both a 2D synthetic image dataset and a 3D synthetic point cloud dataset. Our work is a sub-task in the framework of a remanufacturing project, in which small electric motors are used as fundamental objects. Apart from the given detection, classification, and segmentation annotations, the key objects also have multiple learnable attributes with ground truth provided. This benchmark can be used for computer vision tasks including 2D/3D detection, classification, segmentation, and multi-attribute learning. It is worth mentioning that most attributes of the motors are quantified as continuously variable rather than binary, which makes our benchmark well-suited for the less explored regression tasks. In addition, appropriate evaluation metrics are adopted or developed for each task and promising baseline results are provided. We hope this benchmark can stimulate more research efforts on the sub-domain of object attribute learning and multi-task learning in the future.