Laplacian Pyramid Reconstruction and Refinement for Semantic Segmentation
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Sharing features improves accuracy by a small margin.
“Similar to [29], we find that sharing features improves accuracy by a small margin.”
From Feature Pyramid Networks for Object Detection (2017) · cites this paper as [29]
Section 4 (p. 8) reports modest accuracy gains from fusing feature maps at multiple resolutions, although adding the 4x layer yields no gain or a decrease. That supports the direction and small size of the reported improvement, but the paper describes feature fusion rather than an experiment on sharing features. (LLM, conf=0.76)
Experimental — accuracy not yet measuredFast and Faster R-CNN opt to not use featurized image pyramids under default settings.
“For these reasons, Fast and Faster R-CNN [11, 29] opt to not use featurized image pyramids under default settings.”
From Feature Pyramid Networks for Object Detection (2017) · cites this paper as [29]
The Related Work section (p. 3) discusses multi-resolution features for semantic segmentation, and the results (p. 8) discuss multi-scale testing of the authors’ model. Neither passage addresses whether Fast or Faster R-CNN omit featurized image pyramids by default; searches for those models and that setting found no supporting passage in our copy. (LLM, conf=0.77)
Experimental — accuracy not yet measuredFor fair comparisons with original RPNs, the baselines use the same hyper-parameters as FPN.
“For fair comparisons with original RPNs [29], we run two baselines (Table 1(a, b)) using the single-scale map of C 4 (the same as [16]) or C 5 , both using the same hyper-parameters as ours, including using 5 scale anchors of { 32 2 , 64 2 , 128 2 , 256 2 , 512 2 } .”
From Feature Pyramid Networks for Object Detection (2017) · cites this paper as [29]
The citation is attached to “original RPNs,” but our copy describes an LRR semantic-segmentation model, not an RPN (Introduction, p. 2). Its comparison with an FCN baseline (Section 5.2, p. 10) does not address the claimed C4/C5 baselines, FPN hyper-parameters, or anchor scales; searches for those terms found no supporting passage. (LLM, conf=0.78)
Experimental — accuracy not yet measured
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Semantic Video CNNs Through Representation Warping
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Semantic Correlation Promoted Shape-Variant Context for Segmentation
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Semantic Segmentation With Context Encoding and Multi-Path Decoding
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Real-Time High-Performance Semantic Image Segmentation of Urban Street Scenes
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Deep-Learning-Based Approaches for Semantic Segmentation of Natural Scene Images: A Review
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- Authors
- Ghiasi, G., & Fowlkes, C. C.
- Affiliations
- University of California, Irvine (US)
- Corresponding author
- Ghiasi, Golnaz
- ORCID
- Fowlkes, Charless C.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2016
- Pages
- 519-534
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2508741746
- Cited by
- 502
- References
- 41
- Citations by year
- 2026: 8 · 2025: 20 · 2024: 29 · 2023: 37 · 2022: 47 · 2021: 67 · 2020: 76 · 2019: 75 · 2018: 79 · 2017: 54 · 2016: 10
- FWCI
- 25.20
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Lecture notes in computer science (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (1253538, 1262547)
- Nvidia
- Topics
- 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
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Keywords
- semantic segmentation, Laplacian pyramid, multi-resolution reconstruction, sub-pixel localization, skip connections, boundary refinement, spatial pooling, feature maps
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