Enhanced Deformable Convolution with Center-invariant Offset and Edge-aware Mask
Deformable convolution networks have recently become popular for many computer vision tasks, especially for semantic segmentation, because of their exceptional capabilities in dynamic spatial modeling. However, due to the dense deformable offsets and the lack of longer-range dependencies, they can not fully adopt proper and precise deformations for feature representations. To tackle the issues, in this paper, we propose Enhanced Deformable ConvNets (EDCN) for semantic segmentation. Specifically, a novel Enhanced Deformable Convolution (EDC) is exploited in the decoder, which integrates the Cen
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- FuzzyOverlapping authors or contributors · 62%affaan-m/ECC →
“Shared author/contributor keys: jiang”
- FuzzyOverlapping authors or contributors · 62%pytorch/pytorch →
“Shared author/contributor keys: zou”
- FuzzyOverlapping authors or contributors · 62%BerriAI/litellm →
“Shared author/contributor keys: jiang”
- FuzzyOverlapping authors or contributors · 62%modular/modular →
“Shared author/contributor keys: liu”
- FuzzyOverlapping authors or contributors · 62%sgl-project/sglang →
“Shared author/contributor keys: zhou”
- LinkedLinked via arxiv author · 85%Yixiao Liu →
“Enhanced Deformable Convolution with Center-invariant Offset and Edge-aware Mask”
- LinkedLinked via arxiv author · 85%Xiaoyuan Yang →
“Enhanced Deformable Convolution with Center-invariant Offset and Edge-aware Mask”
- LinkedLinked via arxiv author · 85%Jin Jiang →
“Enhanced Deformable Convolution with Center-invariant Offset and Edge-aware Mask”