Research map / Video / Video segmentation and tracking
SwiftNet: Real-Time Video 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 variation-aware triggering instead of periodical sampling (5) with full-frame operations (ablation) | SwiftNet PAM · DAVIS 2017 validation | FPS: 35 -> 52 | Helped |
| Use variation-aware triggering instead of periodical sampling (5) with full-frame operations (ablation) | SwiftNet PAM · DAVIS 2017 validation | J&F: 78.2 -> 78.1 | No clear effect |
| Use pixel-wise update and matching instead of full-frame operations with periodical sampling (5) (ablation) | SwiftNet PAM · DAVIS 2017 validation | FPS: 35 -> 65 | Helped |
| Use pixel-wise update and matching instead of full-frame operations with periodical sampling (5) (ablation) | SwiftNet PAM · DAVIS 2017 validation | J&F: 78.2 -> 77.8 | Hurt |
| Use variation-aware triggering instead of periodical sampling (5) with pixel-wise operations (ablation) | SwiftNet PAM · DAVIS 2017 validation | FPS: 65 -> 70 | Helped |
| Use variation-aware triggering instead of periodical sampling (5) with pixel-wise operations (ablation) | SwiftNet PAM · DAVIS 2017 validation | J&F: 77.8 -> 77.8 | No clear effect |
| Use LAE instead of low-level or high-level aggregation (ablation) | SwiftNet reference encoder · DAVIS 2017 validation | J&F: 78.0 -> 78.2; 75.4 -> 78.2 | Mixed |
| Use LAE instead of low-level aggregation (ablation) | SwiftNet reference encoder · DAVIS 2017 validation | FPS: 22 -> 35 | Helped |
| Use LAE instead of high-level aggregation (ablation) | SwiftNet reference encoder · DAVIS 2017 validation | FPS: 37 -> 35 | No clear effect |
Closest papers by meaning
- Efficient Regional Memory Network for Video Object Segmentation (CVPR 2021 · similarity 0.89)
- LiVOS: Light Video Object Segmentation with Gated Linear Matching (CVPR 2025 · similarity 0.88)
- Recurrent Dynamic Embedding for Video Object Segmentation (CVPR 2022 · similarity 0.88)
- Test-time Training for Matching-based Video Object Segmentation (NeurIPS 2023 · similarity 0.88)
- Point-VOS: Pointing Up Video Object Segmentation (CVPR 2024 · similarity 0.87)
- RMem: Restricted Memory Banks Improve Video Object Segmentation (CVPR 2024 · similarity 0.87)
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.