Contextual Priming and Feedback for Faster R-CNN
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The simplest way to improve detection with segmentation is to think of the segmentation network as a fixed feature extractor and integrate it into a detector as auxiliary features.
“To improve detection with segmentation, the simplest way is to think of the segmentation network as a fixed feature extractor and to integrate it into a detector as auxiliary features [83, 192, 193].”
From Object Detection in 20 Years: A Survey (2023) · cites this paper as [193]
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- Authors
- Shrivastava, A., & Gupta, A.
- Affiliations
- Carnegie Mellon University (US)
- Corresponding author
- Shrivastava, Abhinav
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2016
- Pages
- 330-348
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2520951797
- Cited by
- 153
- References
- 78
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 9 · 2023: 9 · 2022: 12 · 2021: 29 · 2020: 27 · 2019: 23 · 2018: 26 · 2017: 10 · 2016: 2
- FWCI
- 7.06
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Lecture notes in computer science (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Keywords
- Faster R-CNN, object detection, semantic segmentation, iterative feedback, two-stage training, region proposal generation
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