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Amodal Instance Segmentation
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Amodal instance segmentation aims to predict the region encompassing both visible and occluded parts of each object.
Description Credit: Amodal Instance Segmentation, ECCV 2016
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Amodal Instance Segmentation with Diffusion Shape Prior Estimation
Foundation Models for Amodal Video Instance Segmentation in Automated Driving
ShapeFormer: Shape Prior Visible-to-Amodal Transformer-based Amodal Instance Segmentation
OBSeg: Accurate and Fast Instance Segmentation Framework Using Segmentation Foundation Models with Oriented Bounding Box Prompts
BLADE: Box-Level Supervised Amodal Segmentation through Directed Expansion
GIN: Generative INvariant Shape Prior for Amodal Instance Segmentation
SynTable: A Synthetic Data Generation Pipeline for Unseen Object Amodal Instance Segmentation of Cluttered Tabletop Scenes
OAFormer: Learning Occlusion Distinguishable Feature for Amodal Instance Segmentation
Amodal Intra-class Instance Segmentation: Synthetic Datasets and Benchmark
AISFormer: Amodal Instance Segmentation with Transformer
Unseen Object Amodal Instance Segmentation via Hierarchical Occlusion Modeling
A Weakly Supervised Amodal Segmenter with Boundary Uncertainty Estimation
Deep Occlusion-Aware Instance Segmentation with Overlapping BiLayers
Learning Vector Quantized Shape Code for Amodal Blastomere Instance Segmentation
Amodal Segmentation through Out-of-Task and Out-of-Distribution Generalization with a Bayesian Model
Layered Embeddings for Amodal Instance Segmentation
Learning Semantics-aware Distance Map with Semantics Layering Network for Amodal Instance Segmentation
Learning to See the Invisible: End-to-End Trainable Amodal Instance Segmentation
Amodal Instance Segmentation
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