Jacquard V2: Refining Datasets using the Human In the Loop Data Correction Method

In the context of rapid advancements in industrial automation, vision-based robotic grasping plays an increasingly crucial role. In order to enhance visual recognition accuracy, the utilization of large-scale datasets is imperative for training models to acquire implicit knowledge related to the handling of various objects. Creating datasets from scratch is a time and labor-intensive process. Moreover, existing datasets often contain errors due to automated annotations aimed at expediency, making the improvement of these datasets a substantial research challenge. Consequently, several …

accuracy automation context cs.ai cs.cv data datasets human human in the loop industrial industrial automation knowledge loop objects recognition robotic role scale training training models vision visual