Research map / Detection and segmentation / Open-set and few-shot detection
Visual Relation Detection using Hybrid Analogical Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use hybrid analogical relation classification instead of the strongest reported baseline | VRD PREDT · VRD | Recall@50: 56.65 -> 52.38 | Hurt |
| Use hybrid analogical relation classification instead of the strongest reported baseline | VRD RELDT · VRD | Recall@100: 32.63 -> 18.41 | Hurt |
| Train relation models with analogical generalization rather than deep-learning relation classifiers | VRD relation detection · VRD train split | training epochs: 7 to 30 epochs -> one epoch | Helped |
Closest papers by meaning
- Few-shot Visual Reasoning with Meta-Analogical Contrastive Learning (NeurIPS 2020 · similarity 0.89)
- MR-NET: Exploiting Mutual Relation for Visual Relationship Detection (AAAI 2019 · similarity 0.88)
- Detecting Visual Relationships With Deep Relational Networks (CVPR 2017 · similarity 0.88)
- Beyond the Doors of Perception: Vision Transformers Represent Relations Between Objects (NeurIPS 2024 · similarity 0.86)
- Groupwise Query Specialization and Quality-Aware Multi-Assignment for Transformer-based Visual Relationship Detection (CVPR 2024 · similarity 0.85)
- Mind the GAP: Glimpse-based Active Perception improves generalization and sample efficiency of visual reasoning (ICLR 2025 · similarity 0.85)
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