Revisiting RCNN: On Awakening the Classification Power of Faster RCNN
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Jointly learning localization and classification may cause sub-optimal solutions for both tasks, and decoupling localization and classification is helpful.
“As pointed out by recent works [7, 43], jointly learning of localization and classification may cause sub-optimal solutions for both tasks, and it is helpful to decouple localization and classification.”
From Learning Spatio-Temporal Transformer for Visual Tracking (2021) · cites this paper as [7]
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Self-citation
- Authors
- Cheng, B., Wei, Y., Shi, H., Feris, R., Xiong, J., & Huang, T.
- Affiliations
- University of Illinois Urbana-Champaign (US)
- IBM (United States) (US)
- Corresponding author
- Wei, Yunchao
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2018
- Pages
- 473-490
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2962731685
- Cited by
- 282
- References
- 34
- Citations by year
- 2026: 8 · 2025: 33 · 2024: 39 · 2023: 36 · 2022: 28 · 2021: 52 · 2020: 43 · 2019: 32 · 2018: 11
- FWCI
- 10.40
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Lecture notes in computer science (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Keywords
- Faster R-CNN, region-based object detection, shared feature representation, multi-task learning, small object detection, receptive fields, object classification
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