Towards Fine-grained Sampling for Active Learning in Object Detection
Abstract
We study the problem of using active learning to reduce annotation effort in training object detectors. Existing efforts in this space ignore the fact that image annotation costs are variable, depending on the number of objects present in a single image. In this regard, we examine a fine-grained sampling based approach for active learning in object detection. Over an unlabeled pool of images, our method aims to selectively pick the most informative subset of bounding boxes (as opposed to full images) to query an annotator. We measure annotation efforts in terms of the number of ground truth bounding boxes obtained. We study the effects of our method on the Feature Pyramid Network and RetinaNet models, and show promising savings in labeling effort to obtain good detection performance.
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Proposed methods do not always fully explore their potential by focusing on an exclusive criterion when considering several complementary sampling strategies simultaneously is possible.
“However, the proposed methods do not always fully explore their potential by focusing on an exclusive criteria-among the previously referenced ones-when considering several complementary sampling strategies simultaneously is possible [36,37,44,45,57,61,67].”
From Ten Years of Active Learning Techniques and Object Detection: A Systematic Review (2023) · cites this paper as [44]
Aggregation of information from multiple sources such as pixel-level, box-level, or image-level has been useful for both uncertainty-based and diversity-based methods.
“Multiple alternatives include the aggregation of information from multiple sources such as: pixel-level, box-level [45] or image-level [44,47], which have been useful for both uncertainty-based and diversity-based methods”
From Ten Years of Active Learning Techniques and Object Detection: A Systematic Review (2023) · cites this paper as [44]
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 56,035 · see citing works
You Only Look Once: Unified, Real-Time Object Detection
Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.91 · Cited by 53,803 · see citing works
Feature Pyramid Networks for Object Detection
Lin, T.-Y., Dollar, P., Girshick, R., He, K., Hariharan, B., & Belongie, S. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.106 · Cited by 30,939 · see citing works
Active Learning for Deep Object Detection
Brust, C.-A., Käding, C., & Denzler, J. (2019) · Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications · doi:10.5220/0007248601810190 · Cited by 78 · see citing works
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Deep Active Learning for Computer Vision: Past and Future
Takezoe, R., Liu, X., Mao, S., Chen, M. T., Feng, Z., Zhang, S., & Wang, X. (2023) · APSIPA Transactions on Signal and Information Processing · doi:10.1561/116.00000057 · Cited by 26 · see citing works
Ten Years of Active Learning Techniques and Object Detection: A Systematic Review
Garcia, D., Carias, J., Adão, T., Jesus, R., Cunha, A., & Magalhães, L. G. (2023) · Applied Sciences · doi:10.3390/app131910667 · Cited by 17 · see citing works
- Authors
- Vikas Desai, S., & Balasubramanian, V. N.
- Affiliations
- Indian Institute of Technology Hyderabad (IN)
- Venue
- 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
- Publisher
- IEEE
- Published
- 2020-06
- Pages
- 4010-4014
- OpenAlex
- W3036121484
- Cited by
- 14
- References
- 5
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 4 · 2023: 4 · 2021: 3
- FWCI
- 0.67
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (publishedVersion)
- Research Archive of Indian Institute of Technology Hyderabad (Indian Institute of Technology Hyderabad) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- active learning (machine learning), object detection, annotation cost reduction, query cost, feature pyramid network, RetinaNet
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