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

• URL: http://arxiv.org/abs/2402.05747v1
• Date: Thu, 8 Feb 2024 15:32:22 GMT
• Title: Jacquard V2: Refining Datasets using the Human In the Loop Data Correction Method
• Authors: Qiuhao Li and Shenghai Yuan
• Abstract summary: We propose utilizing a Human-In-The-Loop(HIL) method to enhance dataset quality. This approach relies on backbone deep learning networks to predict object positions and orientations for robotic grasping. Images lacking labels are augmented with valid grasp bounding box information, whereas images afflicted by catastrophic labeling errors are completely removed.
• Score: 8.588472253340859
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 issues have been identified in the annotation of grasp bounding boxes within the popular Jacquard Grasp. We propose utilizing a Human-In-The-Loop(HIL) method to enhance dataset quality. This approach relies on backbone deep learning networks to predict object positions and orientations for robotic grasping. Predictions with Intersection over Union (IOU) values below 0.2 undergo an assessment by human operators. After their evaluation, the data is categorized into False Negatives(FN) and True Negatives(TN). FN are then subcategorized into either missing annotations or catastrophic labeling errors. Images lacking labels are augmented with valid grasp bounding box information, whereas images afflicted by catastrophic labeling errors are completely removed. The open-source tool Labelbee was employed for 53,026 iterations of HIL dataset enhancement, leading to the removal of 2,884 images and the incorporation of ground truth information for 30,292 images. The enhanced dataset, named the Jacquard V2 Grasping Dataset, served as the training data for a range of neural networks.

Related papers

• CovarNav: Machine Unlearning via Model Inversion and Covariance Navigation [11.222501077070765]
  Machine unlearning has emerged as an essential technique to selectively remove the influence of specific training data points on trained models. We introduce a three-step process, named CovarNav, to facilitate this forgetting. We rigorously evaluate CovarNav on the CIFAR-10 and Vggface2 datasets.
  arXiv  Detail & Related papers  (2023-11-21T21:19:59Z)
• Knowledge Combination to Learn Rotated Detection Without Rotated Annotation [53.439096583978504]
  Rotated bounding boxes drastically reduce output ambiguity of elongated objects. Despite the effectiveness, rotated detectors are not widely employed. We propose a framework that allows the model to predict precise rotated boxes.
  arXiv  Detail & Related papers  (2023-04-05T03:07:36Z)
• Combating noisy labels in object detection datasets [0.0]
  We introduce the Confident Learning for Object Detection (CLOD) algorithm for assessing the quality of each label in object detection datasets. We identify missing, spurious, mislabeled, and mislocated bounding boxes and suggesting corrections. The proposed method is able to point out nearly 80% of artificially disturbed bounding boxes with a false positive rate below 0.1.
  arXiv  Detail & Related papers  (2022-11-25T10:05:06Z)
• Cluster-level pseudo-labelling for source-free cross-domain facial expression recognition [94.56304526014875]
  We propose the first Source-Free Unsupervised Domain Adaptation (SFUDA) method for Facial Expression Recognition (FER) Our method exploits self-supervised pretraining to learn good feature representations from the target data. We validate the effectiveness of our method in four adaptation setups, proving that it consistently outperforms existing SFUDA methods when applied to FER.
  arXiv  Detail & Related papers  (2022-10-11T08:24:50Z)
• Incorporating Semi-Supervised and Positive-Unlabeled Learning for Boosting Full Reference Image Quality Assessment [73.61888777504377]
  Full-reference (FR) image quality assessment (IQA) evaluates the visual quality of a distorted image by measuring its perceptual difference with pristine-quality reference. Unlabeled data can be easily collected from an image degradation or restoration process, making it encouraging to exploit unlabeled training data to boost FR-IQA performance. In this paper, we suggest to incorporate semi-supervised and positive-unlabeled (PU) learning for exploiting unlabeled data while mitigating the adverse effect of outliers.
  arXiv  Detail & Related papers  (2022-04-19T09:10:06Z)
• Weakly Supervised Change Detection Using Guided Anisotropic Difusion [97.43170678509478]
  We propose original ideas that help us to leverage such datasets in the context of change detection. First, we propose the guided anisotropic diffusion (GAD) algorithm, which improves semantic segmentation results. We then show its potential in two weakly-supervised learning strategies tailored for change detection.
  arXiv  Detail & Related papers  (2021-12-31T10:03:47Z)
• Attentive Prototypes for Source-free Unsupervised Domain Adaptive 3D Object Detection [85.11649974840758]
  3D object detection networks tend to be biased towards the data they are trained on. We propose a single-frame approach for source-free, unsupervised domain adaptation of lidar-based 3D object detectors.
  arXiv  Detail & Related papers  (2021-11-30T18:42:42Z)
• Training Dynamic based data filtering may not work for NLP datasets [0.0]
  We study the applicability of the Area Under the Margin (AUM) metric to identify mislabelled examples in NLP datasets. We find that mislabelled samples can be filtered using the AUM metric in NLP datasets but it also removes a significant number of correctly labeled points.
  arXiv  Detail & Related papers  (2021-09-19T18:50:45Z)
• Handling Missing Data with Graph Representation Learning [62.59831675688714]
  We propose GRAPE, a graph-based framework for feature imputation as well as label prediction. Under GRAPE, the feature imputation is formulated as an edge-level prediction task and the label prediction as a node-level prediction task. Experimental results on nine benchmark datasets show that GRAPE yields 20% lower mean absolute error for imputation tasks and 10% lower for label prediction tasks.
  arXiv  Detail & Related papers  (2020-10-30T17:59:13Z)
• Supervised Visualization for Data Exploration [9.742277703732187]
  We describe a novel supervised visualization technique based on random forest proximities and diffusion-based dimensionality reduction. Our approach is robust to noise and parameter tuning, thus making it simple to use while producing reliable visualizations for data exploration.
  arXiv  Detail & Related papers  (2020-06-15T19:10:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.