Research map / Detection and segmentation / Open-set and few-shot detection
Tuning-Free Amodal Segmentation via the Occlusion-Free Bias of Inpainting Models
Read the paperCheck 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.
Closest papers by meaning
- pix2gestalt: Amodal Segmentation by Synthesizing Wholes (CVPR 2024 · similarity 0.92)
- Amodal Ground Truth and Completion in the Wild (CVPR 2024 · similarity 0.91)
- Towards Efficient Foundation Model for Zero-shot Amodal Segmentation (CVPR 2025 · similarity 0.91)
- Using Diffusion Priors for Video Amodal Segmentation (CVPR 2025 · similarity 0.91)
- Segment Anything, Even Occluded (CVPR 2025 · similarity 0.90)
- BLADE: Box-Level Supervised Amodal Segmentation through Directed Expansion (AAAI 2024 · similarity 0.90)
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.
