Bones Can't Be Triangles: Accurate and Efficient Vertebrae Keypoint Estimation through Collaborative Error Revision
- URL: http://arxiv.org/abs/2409.03261v1
- Date: Thu, 5 Sep 2024 06:03:52 GMT
- Title: Bones Can't Be Triangles: Accurate and Efficient Vertebrae Keypoint Estimation through Collaborative Error Revision
- Abstract summary: KeyBot is designed to identify and correct significant and typical errors in existing models, akin to user revision.
By characterizing typical error types and using simulated errors for training, KeyBot effectively corrects these errors and significantly reduces user workload.
- Score: 39.347666307218006
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Recent advances in interactive keypoint estimation methods have enhanced accuracy while minimizing user intervention. However, these methods require user input for error correction, which can be costly in vertebrae keypoint estimation where inaccurate keypoints are densely clustered or overlap. We introduce a novel approach, KeyBot, specifically designed to identify and correct significant and typical errors in existing models, akin to user revision. By characterizing typical error types and using simulated errors for training, KeyBot effectively corrects these errors and significantly reduces user workload. Comprehensive quantitative and qualitative evaluations on three public datasets confirm that KeyBot significantly outperforms existing methods, achieving state-of-the-art performance in interactive vertebrae keypoint estimation. The source code and demo video are available at: https://ts-kim.github.io/KeyBot/
Related papers
- Rethinking Early Stopping: Refine, Then Calibrate [49.966899634962374]
We show that calibration error and refinement error are not minimized simultaneously during training.
We introduce a new metric for early stopping and hyper parameter tuning that makes it possible to minimize refinement error during training.
Our method integrates seamlessly with any architecture and consistently improves performance across diverse classification tasks.
arXiv Detail & Related papers (2025-01-31T15:03:54Z) - An Efficient Error Estimation Method in Quantum Key Distribution [5.433638656301075]
We propose a hybrid approach to preserve the entire sifted key after error estimation.
Although accessible to an eavesdropper, the modified and extended sifted key ensures that the number of attempts to crack it remains the same.
arXiv Detail & Related papers (2024-11-11T17:37:20Z) - Bridging Precision and Confidence: A Train-Time Loss for Calibrating
Object Detection [58.789823426981044]
We propose a novel auxiliary loss formulation that aims to align the class confidence of bounding boxes with the accurateness of predictions.
Our results reveal that our train-time loss surpasses strong calibration baselines in reducing calibration error for both in and out-domain scenarios.
arXiv Detail & Related papers (2023-03-25T08:56:21Z) - MDPose: Real-Time Multi-Person Pose Estimation via Mixture Density Model [27.849059115252008]
We propose a novel framework of single-stage instance-aware pose estimation by modeling the joint distribution of human keypoints.
Our MDPose achieves state-of-the-art performance by successfully learning the high-dimensional joint distribution of human keypoints.
arXiv Detail & Related papers (2023-02-17T08:29:33Z) - Class-wise and reduced calibration methods [0.0]
We show how a reduced calibration method transforms the original problem into a simpler one.
Second, we propose class-wise calibration methods, based on building on a phenomenon called neural collapse.
Applying the two methods together results in class-wise reduced calibration algorithms, which are powerful tools for reducing the prediction and per-class calibration errors.
arXiv Detail & Related papers (2022-10-07T17:13:17Z) - Is the Performance of My Deep Network Too Good to Be True? A Direct
Approach to Estimating the Bayes Error in Binary Classification [86.32752788233913]
In classification problems, the Bayes error can be used as a criterion to evaluate classifiers with state-of-the-art performance.
We propose a simple and direct Bayes error estimator, where we just take the mean of the labels that show emphuncertainty of the classes.
Our flexible approach enables us to perform Bayes error estimation even for weakly supervised data.
arXiv Detail & Related papers (2022-02-01T13:22:26Z) - BDC: Bounding-Box Deep Calibration for High Performance Face Detection [11.593495085674345]
Modern CNN-based face detectors have achieved tremendous strides due to large annotated datasets.
misaligned results with high detection confidence but low localization accuracy restrict the further improvement of detection performance.
We propose a novel Bounding-Box Deep (BDC) method to reasonably replace inconsistent annotations with model predicted bounding-boxes.
arXiv Detail & Related papers (2021-10-08T04:41:41Z) - ALT-MAS: A Data-Efficient Framework for Active Testing of Machine
Learning Algorithms [58.684954492439424]
We propose a novel framework to efficiently test a machine learning model using only a small amount of labeled test data.
The idea is to estimate the metrics of interest for a model-under-test using Bayesian neural network (BNN)
arXiv Detail & Related papers (2021-04-11T12:14:04Z) - Fast Uncertainty Quantification for Deep Object Pose Estimation [91.09217713805337]
Deep learning-based object pose estimators are often unreliable and overconfident.
In this work, we propose a simple, efficient, and plug-and-play UQ method for 6-DoF object pose estimation.
arXiv Detail & Related papers (2020-11-16T06:51:55Z) - Evaluating Prediction-Time Batch Normalization for Robustness under
Covariate Shift [81.74795324629712]
We call prediction-time batch normalization, which significantly improves model accuracy and calibration under covariate shift.
We show that prediction-time batch normalization provides complementary benefits to existing state-of-the-art approaches for improving robustness.
The method has mixed results when used alongside pre-training, and does not seem to perform as well under more natural types of dataset shift.
arXiv Detail & Related papers (2020-06-19T05:08:43Z)
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.