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Amodal Instance Segmentation

Amodal instance segmentation aims to predict the region encompassing both visible and occluded parts of each object.

Description Credit: Amodal Instance Segmentation, ECCV 2016

Papers

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Showing 1–24 of 24 papers

TitleStatusHype
OBSeg: Accurate and Fast Instance Segmentation Framework Using Segmentation Foundation Models with Oriented Bounding Box PromptsCode2
WALT: Watch and Learn 2D Amodal Representation From Time-Lapse ImageryCode1
Amodal Segmentation through Out-of-Task and Out-of-Distribution Generalization with a Bayesian ModelCode1
Unseen Object Amodal Instance Segmentation via Hierarchical Occlusion ModelingCode1
Deep Occlusion-Aware Instance Segmentation with Overlapping BiLayersCode1
Amodal Intra-class Instance Segmentation: Synthetic Datasets and BenchmarkCode1
A Weakly Supervised Amodal Segmenter with Boundary Uncertainty EstimationCode1
AISFormer: Amodal Instance Segmentation with TransformerCode1
SynTable: A Synthetic Data Generation Pipeline for Unseen Object Amodal Instance Segmentation of Cluttered Tabletop ScenesCode1
2D Amodal Instance Segmentation Guided by 3D Shape Prior—0
Amodal Instance Segmentation—0
Amodal Instance Segmentation with Diffusion Shape Prior Estimation—0
BLADE: Box-Level Supervised Amodal Segmentation through Directed Expansion—0
GIN: Generative INvariant Shape Prior for Amodal Instance Segmentation—0
Learning Vector Quantized Shape Code for Amodal Blastomere Instance Segmentation—0
MUVA: A New Large-Scale Benchmark for Multi-View Amodal Instance Segmentation in the Shopping Scenario—0
OAFormer: Learning Occlusion Distinguishable Feature for Amodal Instance Segmentation—0
Segment Anything, Even Occluded—0
Amodal Instance Segmentation With KINS DatasetCode0
ShapeFormer: Shape Prior Visible-to-Amodal Transformer-based Amodal Instance SegmentationCode0
Learning to See the Invisible: End-to-End Trainable Amodal Instance SegmentationCode0
Learning Semantics-aware Distance Map with Semantics Layering Network for Amodal Instance SegmentationCode0
Layered Embeddings for Amodal Instance SegmentationCode0
Foundation Models for Amodal Video Instance Segmentation in Automated DrivingCode0
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