Summary of Jacquard V2: Refining Datasets Using the Human in the Loop Data Correction Method, by Qiuhao Li and Shenghai Yuan
Jacquard V2: Refining Datasets using the Human In the Loop Data Correction Method
by Qiuhao Li, Shenghai Yuan
First submitted to arxiv on: 8 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Human-In-The-Loop (HIL) method enhances the quality of large-scale datasets used for training models in vision-based robotic grasping. The current state-of-the-art Jacquard Grasp dataset contains errors due to automated annotations, making it challenging to improve its accuracy. The HIL approach utilizes deep learning networks to predict object positions and orientations, which are then evaluated by human operators. This process identifies False Negatives (FN) and True Negatives (TN), allowing for the removal of catastrophic labeling errors and augmentation with valid grasp bounding box information. The open-source tool Labelbee is employed to enhance the dataset, leading to the creation of the Jacquard V2 Grasping Dataset, which serves as training data for various neural networks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re working on robots that can pick up objects. To teach these robots what things look like and how to grasp them, we need big datasets with lots of examples. But making these datasets is hard work! And often, the computers trying to help us create these datasets make mistakes. That’s why we developed a new way to improve our dataset quality. It involves using computer algorithms to predict where objects are and what they look like, but then humans check those predictions to make sure they’re accurate. This helps us fix mistakes and get rid of bad data. After lots of work with this process, we ended up with an even better dataset that can help train robots to grasp things more accurately. |
Keywords
» Artificial intelligence » Bounding box » Deep learning
