Research map / Label-efficient and robust learning / Domain adaptation and generalization
Learning to Generalize Unseen Domains via Memory-based Multi-Source Meta-Learning for Person Re-Identification
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 |
|---|---|---|---|
| Use multi-source meta-learning with memory identification and MetaBN instead of the matched QAConv baseline | ReID trained on three source domains · Market-1501 | mAP: 35.6 -> 50.2 | Helped |
| Use multi-source meta-learning with memory identification and MetaBN instead of the matched QAConv baseline | ReID trained on three source domains · DukeMTMC-reID | mAP: 47.1 -> 51.1 | Helped |
| Use multi-source meta-learning with memory identification and MetaBN instead of the matched QAConv baseline | ReID trained on three source domains · CUHK-NP | mAP: 21.0 -> 32.1 | Helped |
| Use multi-source meta-learning with memory identification and MetaBN instead of the matched QAConv baseline | ReID trained on three source domains · MSMT17 | mAP: 7.5 -> 14.7 | Helped |
| Add meta-learning optimization (ablation) | ResNet-50 M³L · Market-1501 | Rank-1: 67.9 -> 73.2 | Helped |
| Add meta-learning optimization (ablation) | IBN-Net50 M³L · Market-1501 | mAP: 43.6 -> 49.0 | Helped |
| Add MetaBN to meta-learning (ablation) | ResNet-50 M³L · Market-1501 | Rank-1: 73.2 -> 74.5 | Helped |
| Add MetaBN to meta-learning (ablation) | IBN-Net50 M³L · CUHK03 | mAP: 31.0 -> 32.1 | Helped |
| Add triplet loss alongside memory-based identification loss (ablation) | ResNet-50 M³L · Market-1501 | mAP: 47.8 -> 48.1 | Helped |
| Use memory-based identification loss instead of triplet loss only (ablation) | ResNet-50 M³L · Market-1501 | mAP: 35.1 -> 47.8 | Helped |
| Use a memory-based classifier rather than a global FC classifier during meta-learning (ablation) | ResNet-50 M³L · Market-1501 | mAP: 39.7 -> 48.1 | Helped |
| Use a memory-based classifier rather than parallel FC classifiers during meta-learning (ablation) | ResNet-50 M³L · Market-1501 | mAP: 40.9 -> 48.1 | Helped |
Closest papers by meaning
- Meta Batch-Instance Normalization for Generalizable Person Re-Identification (CVPR 2021 · similarity 0.92)
- Person30K: A Dual-Meta Generalization Network for Person Re-Identification (CVPR 2021 · similarity 0.91)
- Meta Distribution Alignment for Generalizable Person Re-Identification (CVPR 2022 · similarity 0.91)
- Generalizable Person Re-Identification by Domain-Invariant Mapping Network (CVPR 2019 · similarity 0.90)
- Generalizable Person Re-identification via Balancing Alignment and Uniformity (NeurIPS 2024 · similarity 0.87)
- Exploring the Camera Bias of Person Re-identification (ICLR 2025 · similarity 0.86)
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.