Research map / Detection and segmentation / Open-set and few-shot detection
Tensorize, Factorize and Regularize: Robust Visual Relationship Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add the tensor-factorization relational prior to visual relationship detection | Proposed method · Scene Graph, zero-shot relationship detection | recall: ∼40% | Helped |
| Add the tensor-factorization relational prior to the scene graph pipeline | Visual Genome scene graph prediction · PredCls | R@20: at most ∼30% | Helped |
| Add the tensor-factorization relational prior to the scene graph pipeline | Visual Genome scene graph prediction · SgGen | recall: ∼10% | Helped |
| Use the relational learning module (ablation) | Visual Genome scene graph prediction · PredCls, SgCls, SgGen | recall | Helped |
| Set prior-application probability to θ = 0.2 rather than higher tested values (ablation) | Visual Genome scene graph pipeline · Visual Genome | recall | Helped |
Closest papers by meaning
- Visual Relationship Detection with Low Rank Non-Negative Tensor Decomposition (AAAI 2020 · similarity 0.92)
- Visual Relationship Detection With Deep Structural Ranking (AAAI 2018 · similarity 0.91)
- Large-Scale Visual Relationship Understanding (AAAI 2019 · similarity 0.90)
- Detecting Visual Relationships With Deep Relational Networks (CVPR 2017 · similarity 0.90)
- Groupwise Query Specialization and Quality-Aware Multi-Assignment for Transformer-based Visual Relationship Detection (CVPR 2024 · similarity 0.88)
- DSGG: Dense Relation Transformer for an End-to-end Scene Graph Generation (CVPR 2024 · similarity 0.87)
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