Research map / Detection and segmentation / CNN features
Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes
Check your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use FRRUs with full-resolution residual and pooling streams instead of residual units with long-range skip connections | FRRN A · Cityscapes validation set, quarter resolution | mean IoU: 65.7% vs. 62.8% | Helped |
| Add fully connected CRF post-processing | FRRN · Cityscapes validation set | IoU: ∼0.5% | Helped |
Closest papers by meaning
- In Defense of Pre-Trained ImageNet Architectures for Real-Time Semantic Segmentation of Road-Driving Images (CVPR 2019 · similarity 0.90)
- Rethinking Semantic Segmentation From a Sequence-to-Sequence Perspective With Transformers (CVPR 2021 · similarity 0.89)
- The Cityscapes Dataset for Semantic Urban Scene Understanding (CVPR 2016 · similarity 0.88)
- Progressive Neighborhood Aggregation for Semantic Segmentation Refinement (AAAI 2023 · similarity 0.87)
- Weakly Supervised Semantic Segmentation for Driving Scenes (AAAI 2024 · similarity 0.87)
- Learning Content-Enhanced Mask Transformer for Domain Generalized Urban-Scene Segmentation (AAAI 2024 · similarity 0.86)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.