Soft-labeling Strategies for Rapid Sub-Typing
- URL: http://arxiv.org/abs/2209.12684v1
- Date: Fri, 23 Sep 2022 03:04:27 GMT
- Title: Soft-labeling Strategies for Rapid Sub-Typing
- Authors: Grant Rosario, David Noever, and Matt Ciolino
- Abstract summary: This research provides a new method for automated data collection, curation, labeling, and iterative training with minimal human intervention.
The new operational scale effectively scanned an entire city (68 square miles) in grid search and yielded a prediction of car color from space observations.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The challenge of labeling large example datasets for computer vision
continues to limit the availability and scope of image repositories. This
research provides a new method for automated data collection, curation,
labeling, and iterative training with minimal human intervention for the case
of overhead satellite imagery and object detection. The new operational scale
effectively scanned an entire city (68 square miles) in grid search and yielded
a prediction of car color from space observations. A partially trained yolov5
model served as an initial inference seed to output further, more refined model
predictions in iterative cycles. Soft labeling here refers to accepting label
noise as a potentially valuable augmentation to reduce overfitting and enhance
generalized predictions to previously unseen test data. The approach takes
advantage of a real-world instance where a cropped image of a car can
automatically receive sub-type information as white or colorful from pixel
values alone, thus completing an end-to-end pipeline without overdependence on
human labor.
Related papers
- Rethinking Transformers Pre-training for Multi-Spectral Satellite
Imagery [78.43828998065071]
Recent advances in unsupervised learning have demonstrated the ability of large vision models to achieve promising results on downstream tasks.
Such pre-training techniques have also been explored recently in the remote sensing domain due to the availability of large amount of unlabelled data.
In this paper, we re-visit transformers pre-training and leverage multi-scale information that is effectively utilized with multiple modalities.
arXiv Detail & Related papers (2024-03-08T16:18:04Z) - Task Specific Pretraining with Noisy Labels for Remote Sensing Image Segmentation [18.598405597933752]
Self-supervision provides remote sensing a tool to reduce the amount of exact, human-crafted geospatial annotations.
In this work, we propose to exploit noisy semantic segmentation maps for model pretraining.
The results from two datasets indicate the effectiveness of task-specific supervised pretraining with noisy labels.
arXiv Detail & Related papers (2024-02-25T18:01:42Z) - LabelFormer: Object Trajectory Refinement for Offboard Perception from
LiDAR Point Clouds [37.87496475959941]
"Auto-labelling" offboard perception models are trained to automatically generate annotations from raw LiDAR point clouds.
We propose LabelFormer, a simple, efficient, and effective trajectory-level refinement approach.
Our approach first encodes each frame's observations separately, then exploits self-attention to reason about the trajectory with full temporal context.
arXiv Detail & Related papers (2023-11-02T17:56:06Z) - Semi-Supervised Learning for hyperspectral images by non parametrically
predicting view assignment [25.198550162904713]
Hyperspectral image (HSI) classification is gaining a lot of momentum in present time because of high inherent spectral information within the images.
Recently, to effectively train the deep learning models with minimal labelled samples, the unlabeled samples are also being leveraged in self-supervised and semi-supervised setting.
In this work, we leverage the idea of semi-supervised learning to assist the discriminative self-supervised pretraining of the models.
arXiv Detail & Related papers (2023-06-19T14:13:56Z) - Transfer Learning Application of Self-supervised Learning in ARPES [12.019651078748236]
Development in angle-resolved photoemission spectroscopy (ARPES) technique involves spatially resolving samples.
One of it is to label similar dispersion cuts and map them spatially.
In this work, we demonstrate that the recent development in representational learning model combined with k-means clustering can help automate that part of data analysis.
arXiv Detail & Related papers (2022-08-23T11:58:05Z) - Self-Supervised Learning as a Means To Reduce the Need for Labeled Data
in Medical Image Analysis [64.4093648042484]
We use a dataset of chest X-ray images with bounding box labels for 13 different classes of anomalies.
We show that it is possible to achieve similar performance to a fully supervised model in terms of mean average precision and accuracy with only 60% of the labeled data.
arXiv Detail & Related papers (2022-06-01T09:20:30Z) - Visual Distant Supervision for Scene Graph Generation [66.10579690929623]
Scene graph models usually require supervised learning on large quantities of labeled data with intensive human annotation.
We propose visual distant supervision, a novel paradigm of visual relation learning, which can train scene graph models without any human-labeled data.
Comprehensive experimental results show that our distantly supervised model outperforms strong weakly supervised and semi-supervised baselines.
arXiv Detail & Related papers (2021-03-29T06:35:24Z) - Data Augmentation for Object Detection via Differentiable Neural
Rendering [71.00447761415388]
It is challenging to train a robust object detector when annotated data is scarce.
Existing approaches to tackle this problem include semi-supervised learning that interpolates labeled data from unlabeled data.
We introduce an offline data augmentation method for object detection, which semantically interpolates the training data with novel views.
arXiv Detail & Related papers (2021-03-04T06:31:06Z) - Semi-Automatic Data Annotation guided by Feature Space Projection [117.9296191012968]
We present a semi-automatic data annotation approach based on suitable feature space projection and semi-supervised label estimation.
We validate our method on the popular MNIST dataset and on images of human intestinal parasites with and without fecal impurities.
Our results demonstrate the added-value of visual analytics tools that combine complementary abilities of humans and machines for more effective machine learning.
arXiv Detail & Related papers (2020-07-27T17:03:50Z) - Transferring and Regularizing Prediction for Semantic Segmentation [115.88957139226966]
In this paper, we exploit the intrinsic properties of semantic segmentation to alleviate such problem for model transfer.
We present a Regularizer of Prediction Transfer (RPT) that imposes the intrinsic properties as constraints to regularize model transfer in an unsupervised fashion.
Extensive experiments are conducted to verify the proposal of RPT on the transfer of models trained on GTA5 and SYNTHIA (synthetic data) to Cityscapes dataset (urban street scenes)
arXiv Detail & Related papers (2020-06-11T16:19:41Z) - Discoverability in Satellite Imagery: A Good Sentence is Worth a
Thousand Pictures [0.0]
Small satellite constellations provide daily global coverage of the earth's landmass.
To extract text annotations from raw pixels requires two dependent machine learning models.
We evaluate seven models on the previously largest benchmark for satellite image captions.
arXiv Detail & Related papers (2020-01-03T20:41:18Z)
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.