Corpus ID: 102480348

GFF: Gated Fully Fusion for Semantic Segmentation

@article{Li2019GFFGF,
  title={GFF: Gated Fully Fusion for Semantic Segmentation},
  author={Xiangtai Li and Houlong Zhao and Lei Han and Yunhai Tong and Kuiyuan Yang},
  journal={ArXiv},
  year={2019},
  volume={abs/1904.01803}
}
Semantic segmentation generates comprehensive understanding of scenes at a semantic level through densely predicting the category for each pixel. [...] Key Method Specifically, features at each level are enhanced by higher-level features with stronger semantics and lower-level features with more details, and gates are used to control the propagation of useful information which significantly reduces the noises during fusion. We achieve the state of the art results on two challenging scene understanding datasets…Expand
CARNet: Context Attention Refine Network for Semantic Segmentation
Learning More Accurate Features for Semantic Segmentation in CycleNet
Dynamic Dual Sampling Module for Fine-Grained Semantic Segmentation
Bidirectional Pyramid Networks for Semantic Segmentation
RFBNet: Deep Multimodal Networks with Residual Fusion Blocks for RGB-D Semantic Segmentation
Deep multimodal fusion for semantic image segmentation: A survey
AttaNet: Attention-Augmented Network for Fast and Accurate Scene Parsing
GFNet: Gate Fusion Network With Res2Net for Detecting Salient Objects in RGB-D Images
Scene Segmentation With Dual Relation-Aware Attention Network
...
1
2
3
...

References

SHOWING 1-10 OF 64 REFERENCES
ExFuse: Enhancing Feature Fusion for Semantic Segmentation
RefineNet: Multi-path Refinement Networks for High-Resolution Semantic Segmentation
DenseASPP for Semantic Segmentation in Street Scenes
Context Encoding for Semantic Segmentation
Fully Convolutional Networks for Semantic Segmentation
Understanding Convolution for Semantic Segmentation
Dual Attention Network for Scene Segmentation
Dense Decoder Shortcut Connections for Single-Pass Semantic Segmentation
...
1
2
3
4
5
...