Research map / Detection and segmentation / CNN features
Dense Decoder Shortcut Connections for Single-Pass Semantic Segmentation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add dense decoder shortcut connections (ablation) | Ours-101 · NYUD | mean IoU: 46.3 -> 48.1 | Helped |
| Add context aggregation (convolution-pooling blocks) (ablation) | Ours-101 · NYUD | mean IoU: 46.4 -> 48.1 | Helped |
| Add dense decoder shortcut connections (ablation) | RefineNet-101 · NYUD | mean IoU: 43.6 -> 44.7 | Helped |
| Use the proposed ResNeXt-style modules and decoder rather than RefineNet-101 | Semantic segmentation on NYUD · NYUD | mean IoU: 43.6 -> 48.1 | Helped |
| Use single-scale evaluation rather than multi-scale evaluation | Ours · Pascal VOC 2012 | mean IoU: 82.6 -> 81.2 | Hurt |
| Use single-scale evaluation rather than multi-scale evaluation | Ours · Pascal Person-Part | mean IoU: 68.6 vs 68.6 | No clear effect |
Closest papers by meaning
- Decoders Matter for Semantic Segmentation: Data-Dependent Decoding Enables Flexible Feature Aggregation (CVPR 2019 · similarity 0.90)
- Dynamic Neural Representational Decoders for High-Resolution Semantic Segmentation (NeurIPS 2021 · similarity 0.90)
- Learning Fully Dense Neural Networks for Image Semantic Segmentation (AAAI 2019 · similarity 0.89)
- SED: A Simple Encoder-Decoder for Open-Vocabulary Semantic Segmentation (CVPR 2024 · similarity 0.88)
- Rethinking Decoders for Transformer-based Semantic Segmentation: A Compression Perspective (NeurIPS 2024 · similarity 0.88)
- Generalized Decoding for Pixel, Image, and Language (CVPR 2023 · similarity 0.88)
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