How to Efficiently Annotate Images for Best-Performing Deep Learning Based Segmentation Models: An Empirical Study with Weak and Noisy Annotations and Segment Anything Model

• URL: http://arxiv.org/abs/2312.10600v2
• Date: Wed, 20 Dec 2023 22:53:23 GMT
• Title: How to Efficiently Annotate Images for Best-Performing Deep Learning Based Segmentation Models: An Empirical Study with Weak and Noisy Annotations and Segment Anything Model
• Authors: Yixin Zhang, Shen Zhao, Hanxue Gu, Maciej A. Mazurowski
• Abstract summary: Deep neural networks (DNNs) have been deployed for many image segmentation tasks and achieved outstanding performance. preparing a dataset for training segmentations is laborious and costly since typically pixel-level annotations are provided for each object of interest. To alleviate this issue, one can provide only weak labels such as bounding boxes or scribbles, or less accurate (noisy) annotations of the objects.
• Score: 18.293057751504122
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Deep neural networks (DNNs) have been deployed for many image segmentation tasks and achieved outstanding performance. However, preparing a dataset for training segmentation DNNs is laborious and costly since typically pixel-level annotations are provided for each object of interest. To alleviate this issue, one can provide only weak labels such as bounding boxes or scribbles, or less accurate (noisy) annotations of the objects. These are significantly faster to generate and thus result in more annotated images given the same time budget. However, the reduction in quality might negatively affect the segmentation performance of the resulting model. In this study, we perform a thorough cost-effectiveness evaluation of several weak and noisy labels. We considered 11 variants of annotation strategies and 4 datasets. We conclude that the common practice of accurately outlining the objects of interest is virtually never the optimal approach when the annotation time is limited, even if notable annotation time is available (10s of hours). Annotation approaches that stood out in such scenarios were (1) contour-based annotation with rough continuous traces, (2) polygon-based annotation with few vertices, and (3) box annotations combined with the Segment Anything Model (SAM). In situations where unlimited annotation time was available, precise annotations still lead to the highest segmentation model performance.

Related papers

• On-the-Fly Point Annotation for Fast Medical Video Labeling [1.890063512530524]
  In medical research, deep learning models rely on high-quality annotated data. The need to adjust two corners makes the process inherently frame-by-frame. We propose an on-the-fly method for live video annotation to enhance the annotation efficiency.
  arXiv  Detail & Related papers  (2024-04-22T16:59:43Z)
• Learning Tracking Representations from Single Point Annotations [49.47550029470299]
  We propose to learn tracking representations from single point annotations in a weakly supervised manner. Specifically, we propose a soft contrastive learning framework that incorporates target objectness prior to end-to-end contrastive learning.
  arXiv  Detail & Related papers  (2024-04-15T06:50:58Z)
• Deep Active Learning with Noisy Oracle in Object Detection [5.5165579223151795]
  We propose a composite active learning framework including a label review module for deep object detection. We show that utilizing part of the annotation budget to correct the noisy annotations partially in the active dataset leads to early improvements in model performance. In our experiments we achieve improvements of up to 4.5 mAP points of object detection performance by incorporating label reviews at equal annotation budget.
  arXiv  Detail & Related papers  (2023-09-30T13:28:35Z)
• Urban Scene Semantic Segmentation with Low-Cost Coarse Annotation [107.72926721837726]
  coarse annotation is a low-cost but highly effective alternative for training semantic segmentation models. We propose a coarse-to-fine self-training framework that generates pseudo labels for unlabeled regions of coarsely annotated data. Our method achieves a significantly better performance vs annotation cost tradeoff, yielding a comparable performance to fully annotated data with only a small fraction of the annotation budget.
  arXiv  Detail & Related papers  (2022-12-15T15:43:42Z)
• Rethinking Generalization: The Impact of Annotation Style on Medical Image Segmentation [9.056814157662965]
  We show that modeling annotation biases, rather than ignoring them, poses a promising way of accounting for differences in annotation style across datasets. Next, we present an image-conditioning approach to model annotation styles that correlate with specific image features, potentially enabling detection biases to be more easily identified.
  arXiv  Detail & Related papers  (2022-10-31T15:28:49Z)
• A Positive/Unlabeled Approach for the Segmentation of Medical Sequences using Point-Wise Supervision [3.883460584034766]
  We propose a new method to efficiently segment medical imaging volumes or videos using point-wise annotations only. Our approach trains a deep learning model using an appropriate Positive/Unlabeled objective function using point-wise annotations. We show experimentally that our approach outperforms state-of-the-art methods tailored to the same problem.
  arXiv  Detail & Related papers  (2021-07-18T09:13:33Z)
• Towards Good Practices for Efficiently Annotating Large-Scale Image Classification Datasets [90.61266099147053]
  We investigate efficient annotation strategies for collecting multi-class classification labels for a large collection of images. We propose modifications and best practices aimed at minimizing human labeling effort. Simulated experiments on a 125k image subset of the ImageNet100 show that it can be annotated to 80% top-1 accuracy with 0.35 annotations per image on average.
  arXiv  Detail & Related papers  (2021-04-26T16:29:32Z)
• Temporal Calibrated Regularization for Robust Noisy Label Learning [60.90967240168525]
  Deep neural networks (DNNs) exhibit great success on many tasks with the help of large-scale well annotated datasets. However, labeling large-scale data can be very costly and error-prone so that it is difficult to guarantee the annotation quality. We propose a Temporal Calibrated Regularization (TCR) in which we utilize the original labels and the predictions in the previous epoch together.
  arXiv  Detail & Related papers  (2020-07-01T04:48:49Z)
• Improving Semantic Segmentation via Self-Training [75.07114899941095]
  We show that we can obtain state-of-the-art results using a semi-supervised approach, specifically a self-training paradigm. We first train a teacher model on labeled data, and then generate pseudo labels on a large set of unlabeled data. Our robust training framework can digest human-annotated and pseudo labels jointly and achieve top performances on Cityscapes, CamVid and KITTI datasets.
  arXiv  Detail & Related papers  (2020-04-30T17:09:17Z)
• Towards Using Count-level Weak Supervision for Crowd Counting [55.58468947486247]
  This paper studies the problem of weakly-supervised crowd counting which learns a model from only a small amount of location-level annotations (fully-supervised) but a large amount of count-level annotations (weakly-supervised) We devise a simple-yet-effective training strategy, namely Multiple Auxiliary Tasks Training (MATT), to construct regularizes for restricting the freedom of the generated density maps.
  arXiv  Detail & Related papers  (2020-02-29T02:58:36Z)

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.