Research map / Graph learning / Knowledge and temporal graphs
Symbolic Graph Reasoning Meets Convolutions
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add residual connection in SGR layer (ablation) | SGR · Coco-Stuff | mean IoU: 38.1 -> 38.7 | Helped |
| learn separate semantic-to-local mapping weights (ablation) | SGR · Coco-Stuff | mean IoU: 37.2 -> 38.7 | Helped |
| place SGR at res5 instead of res4 (ablation) | SGR · Coco-Stuff | mean IoU: 37.5 -> 38.7 | Helped |
| add second SGR layer at res4 (ablation) | SGR · Coco-Stuff | mean IoU: 38.7 -> 38.8 | No clear effect |
| use concept hierarchy instead of concurrence graph (ablation) | SGR · Coco-Stuff | mean IoU: 38.4 -> 38.7 | Helped |
| use concept hierarchy instead of scene graph (ablation) | SGR · Coco-Stuff | mean IoU: 38.3 -> 38.7 | Helped |
| stack hierarchy and concurrence SGR layers (hybrid) (ablation) | SGR · Coco-Stuff | mean IoU: 38.7 -> 39.1 | Helped |
| transfer SGR layer weights from Coco-Stuff (ablation) | SGR · PASCAL-Context | mean IoU: 51.3 -> 52.5 | Helped |
| add SGR layer to segmentation network | DeepLabv2 (ResNet-101) · ADE20K | mean IoU: 38.97 -> 44.32 | Helped |
| append SGR layer to DenseNet-100 | DenseNet-100 · CIFAR-100 | test error: 22.19 -> 17.68 | Helped |
Closest papers by meaning
- Embedding Symbolic Knowledge into Deep Networks (NeurIPS 2019 · similarity 0.89)
- Distilling Knowledge From Graph Convolutional Networks (CVPR 2020 · similarity 0.89)
- Towards Graph Foundation Models: Training on Knowledge Graphs Enables Transferability to General Graphs (NeurIPS 2025 · similarity 0.88)
- UniGTE: Unified Graph–Text Encoding for Zero-Shot Generalization across Graph Tasks and Domains (NeurIPS 2025 · similarity 0.86)
- GITA: Graph to Visual and Textual Integration for Vision-Language Graph Reasoning (NeurIPS 2024 · similarity 0.86)
- SSTAG: Structure-Aware Self-Supervised Learning Method for Text-Attributed Graphs (NeurIPS 2025 · similarity 0.86)
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