The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
Advanced Deep-Learning Techniques for Salient and Category-Specific Object Detection: A Survey
Abstract
Object detection, including objectness detection (OD), salient object detection (SOD), and category-specific object detection (COD), is one of the most fundamental yet challenging problems in the computer vision community. Over the last several decades, great efforts have been made by researchers to tackle this problem, due to its broad range of applications for other computer vision tasks such as activity or event recognition, content-based image retrieval and scene understanding, etc. While numerous methods have been presented in recent years, a comprehensive review for the proposed high-quality object detection techniques, especially for those based on advanced deep-learning techniques, is still lacking. To this end, this article delves into the recent progress in this research field, including 1) definitions, motivations, and tasks of each subdirection; 2) modern techniques and essential research trends; 3) benchmark data sets and evaluation metrics; and 4) comparisons and analysis of the experimental results. More importantly, we will reveal the underlying relationship among OD, SOD, and COD and discuss in detail some open questions as well as point out several unsolved challenges and promising future works.
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Federated Learning in Mobile Edge Networks: A Comprehensive Survey
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Self-citationAn overview of deep learning in medical imaging focusing on MRI
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Metadata
- Authors
- Han, J., Zhang, D., Cheng, G., Liu, N., & Xu, D.
- Venue
- IEEE Signal Processing Magazine
- Published
- 2018-01
- Volume
- 35
- Issue
- 1
- Pages
- 84-100
- ISSN
- 1053-5888 (ISSN-L)
- 1558-0792
- OpenAlex
- W2783231089
- Cited by
- 621
- References
- 22
- FWCI
- 54.21
- Type
- journal article
- Language
- en
- Open access
- closed
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