Research map / Detection and segmentation / Open-set and few-shot detection
Target-Aware Object Discovery and Association for Unsupervised Video Multi-Object Segmentation
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 the proposed method instead of STEm-Seg | video instance segmentation · YouTube-VIS val | mAP: 35.0 -> 37.1; +2.1% | Helped |
| add the segmentation guidance module (ablation) | D-Net · DAVIS17 val | AP: 50.3 -> 52.0; +1.7% | Helped |
| add target verification (ablation) | T-Net · DAVIS17 val | J&F Mean: 61.3 -> 65.0 | Helped |
| add memory updating (ablation) | T-Net · DAVIS17 val | J&F Mean: 61.2 -> 65.0 | Helped |
Closest papers by meaning
- Track and Segment: An Iterative Unsupervised Approach for Video Object Proposals (CVPR 2016 · similarity 0.91)
- Matching Anything by Segmenting Anything (CVPR 2024 · similarity 0.89)
- Self-supervised Object-Centric Learning for Videos (NeurIPS 2023 · similarity 0.89)
- Prototypical Cross-Attention Networks for Multiple Object Tracking and Segmentation (NeurIPS 2021 · similarity 0.89)
- Learning Spatial-Semantic Features for Robust Video Object Segmentation (ICLR 2025 · similarity 0.89)
- Fast Video Object Segmentation With Temporal Aggregation Network and Dynamic Template Matching (CVPR 2020 · similarity 0.89)
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.