The DOI matches its Crossref record; 159 of the 159 references it cites were found in a registry or an index.
Deep Long-Tailed Learning: A Survey
Open accessAbstract
Deep long-tailed learning, one of the most challenging problems in visual recognition, aims to train well-performing deep models from a large number of images that follow a long-tailed class distribution. In the last decade, deep learning has emerged as a powerful recognition model for learning high-quality image representations and has led to remarkable breakthroughs in generic visual recognition. However, long-tailed class imbalance, a common problem in practical visual recognition tasks, often limits the practicality of deep network based recognition models in real-world applications, since they can be easily biased towards dominant classes and perform poorly on tail classes. To address this problem, a large number of studies have been conducted in recent years, making promising progress in the field of deep long-tailed learning. Considering the rapid evolution of this field, this article aims to provide a comprehensive survey on recent advances in deep long-tailed learning. To be specific, we group existing deep long-tailed learning studies into three main categories (i.e., class re-balancing, information augmentation and module improvement), and review these methods following this taxonomy in detail. Afterward, we empirically analyze several state-of-the-art methods by evaluating to what extent they address the issue of class imbalance via a newly proposed evaluation metric, i.e., relative accuracy. We conclude the survey by highlighting important applications of deep long-tailed learning and identifying several promising directions for future research.
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Artificial intelligence in dentistry-a review
Ding, H., Wu, J., Zhao, W., Matinlinna, J. P., Burrow, M. F., & Tsoi, J. K. H. (2023) · Frontiers in Dental Medicine · doi:10.3389/fdmed.2023.1085251 · Cited by 297
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Logit Inducing With Abnormality Capturing for Semi-Supervised Image Anomaly Detection
Wan, Q., Gao, L., & Li, X. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3205674 · Cited by 22
Understanding the Challenges When 3D Semantic Segmentation Faces Class Imbalanced and OOD Data
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Metadata
- Authors
- Zhang, Y., Kang, B., Hooi, B., Yan, S., & Feng, J.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2023-09-01
- Volume
- 45
- Issue
- 9
- Pages
- 10795-10816
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- 1939-3539
- OpenAlex
- W4366352743
- Cited by
- 769
- References
- 159
- FWCI
- 66.46
- Type
- journal article
- Language
- en
- Open access
- green
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