Res2Net is an image model that employs a variation on bottleneck residual blocks. The motivation is to be able to represent features at multiple scales. This is achieved through a novel building block for CNNs that constructs hierarchical residual-like connections within one single residual block. This represents multi-scale features at a granular level and increases the range of receptive fields for each network layer.
Source: Res2Net: A New Multi-scale Backbone ArchitecturePaper | Code | Results | Date | Stars |
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Task | Papers | Share |
---|---|---|
Speaker Verification | 8 | 22.86% |
Semantic Segmentation | 2 | 5.71% |
Speaker Recognition | 2 | 5.71% |
Retinal Vessel Segmentation | 1 | 2.86% |
Graph Attention | 1 | 2.86% |
Classification | 1 | 2.86% |
Image Segmentation | 1 | 2.86% |
Multi-Task Learning | 1 | 2.86% |
fake voice detection | 1 | 2.86% |