When Simplicity Wins: Bottleneck-Aware Context Modeling for Lightweight Semantic Segmentation
Semantic segmentation demands a careful balance between accuracy, efficiency, and scalability, which remains difficult to achieve for high-resolution imagery. Convolutional networks effectively model local patterns but struggle with long-range dependencies, whereas Vision Transformers capture global context at a high computational cost. While recent work largely focuses on encoder design, the bottleneck stage, central to contextual aggregation and information flow, has been relatively overlooked. We propose SiConMo, a lightweight yet effective framework, implemented in two variants: an RGB-onl
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- FuzzySimilar title/name (fuzzy) · 59%microsoft/semantic-kernel →
“Fuzzy title match (0.73): “When Simplicity Wins: Bottleneck-Aware Context Modeling for ” ≈ “microsoft/semantic-kernel””
- LinkedLinked via arxiv author · 85%Mian Muhammad Naeem Abid →
“When Simplicity Wins: Bottleneck-Aware Context Modeling for Lightweight Semantic Segmentation”
- LinkedLinked via arxiv author · 85%Nancy Mehta →
“When Simplicity Wins: Bottleneck-Aware Context Modeling for Lightweight Semantic Segmentation”
- LinkedLinked via arxiv author · 85%Zongwei Wu →
“When Simplicity Wins: Bottleneck-Aware Context Modeling for Lightweight Semantic Segmentation”
- LinkedLinked via arxiv author · 85%Radu Timofte →
“When Simplicity Wins: Bottleneck-Aware Context Modeling for Lightweight Semantic Segmentation”