Research map / Graph learning / Knowledge and temporal graphs
Scene Labeling Using Sparse Precision Matrix
Check your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add global retrieval probabilities (ablation) | Scene Labeling Using Sparse Precision Matrix · StanfordBG | Avg Accuracy: 72.8 -> 78.9 | Helped |
| Add global retrieval probabilities (ablation) | Scene Labeling Using Sparse Precision Matrix · SIFTflow | Avg Accuracy: 71.2 -> 75.3 | Helped |
| Add global retrieval probabilities (ablation) | Scene Labeling Using Sparse Precision Matrix · MSRC2 | Avg Accuracy: 76.6 -> 77.1 | Helped |
| Add spatial smoothing (ablation) | Scene Labeling Using Sparse Precision Matrix · StanfordBG | Avg Accuracy: 78.9 -> 82.2 | Helped |
| Add spatial smoothing (ablation) | Scene Labeling Using Sparse Precision Matrix · SIFTflow | Avg Accuracy: 75.3 -> 77.7 | Helped |
| Add spatial smoothing (ablation) | Scene Labeling Using Sparse Precision Matrix · MSRC2 | Avg Accuracy: 77.1 -> 81.7 | Helped |
| Replace spatial smoothing with sparse-structure inference (ablation) | Scene Labeling Using Sparse Precision Matrix · StanfordBG | Avg Accuracy: 82.2 -> 84.6 | Helped |
| Replace spatial smoothing with sparse-structure inference (ablation) | Scene Labeling Using Sparse Precision Matrix · SIFTflow | Avg Accuracy: 77.7 -> 80.6 | Helped |
| Replace spatial smoothing with sparse-structure inference (ablation) | Scene Labeling Using Sparse Precision Matrix · MSRC2 | Avg Accuracy: 81.7 -> 84.1 | Helped |
Closest papers by meaning
- Semi-Supervised Clustering Framework for Fine-grained Scene Graph Generation (AAAI 2025 · similarity 0.85)
- Sample and Filter: Nonparametric Scene Parsing via Efficient Filtering (CVPR 2016 · similarity 0.85)
- Conditional Graphical Lasso for Multi-Label Image Classification (CVPR 2016 · similarity 0.85)
- Strongly local p-norm-cut algorithms for semi-supervised learning and local graph clustering (NeurIPS 2020 · similarity 0.84)
- TD²-Net: Toward Denoising and Debiasing for Video Scene Graph Generation (AAAI 2024 · similarity 0.83)
- Tensorized Label Learning on Anchor Graph (AAAI 2024 · similarity 0.83)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.