Amodal Instance Segmentation
13 papers with code • 1 benchmarks • 2 datasets
Different from traditional segmentation which only focuses on visible regions, amodal instance segmentation also predicts the occluded parts of object instances.
Description Credit: Deep Occlusion-Aware Instance Segmentation with Overlapping BiLayers, CVPR'21
Libraries
Use these libraries to find Amodal Instance Segmentation models and implementations
WALT
MOViD-Amodal
Most implemented papers
Learning to See the Invisible: End-to-End Trainable Amodal Instance Segmentation
Semantic amodal segmentation is a recently proposed extension to instance-aware segmentation that includes the prediction of the invisible region of each object instance.
Learning Semantics-aware Distance Map with Semantics Layering Network for Amodal Instance Segmentation
Specifically, we first introduce a new representation, namely a semantics-aware distance map (sem-dist map), to serve as our target for amodal segmentation instead of the commonly used masks and heatmaps.
Amodal Instance Segmentation With KINS Dataset
qqlu/Amodal-Instance-Segmentation-through-KINS-Dataset
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CVPR 2019
We propose the network structure to reason invisible parts via a new multi-task framework with Multi-View Coding (MVC), which combines information in various recognition levels.
Layered Embeddings for Amodal Instance Segmentation
The proposed method extends upon the representational output of semantic instance segmentation by explicitly including both visible and occluded parts.
Amodal Segmentation through Out-of-Task and Out-of-Distribution Generalization with a Bayesian Model
yihongsun/bayesian-amodal
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CVPR 2022
Moreover, by leveraging an outlier process, Bayesian models can further generalize out-of-distribution to segment partially occluded objects and to predict their amodal object boundaries.
Deep Occlusion-Aware Instance Segmentation with Overlapping BiLayers
lkeab/BCNet
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CVPR 2021
Segmenting highly-overlapping objects is challenging, because typically no distinction is made between real object contours and occlusion boundaries.
A Weakly Supervised Amodal Segmenter with Boundary Uncertainty Estimation
ducminhkhoi/Amodal-Instance-Seg-ASBU
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ICCV 2021
The resulting predictions on training images are taken as the pseudo-ground truth for the standard training of Mask-RCNN, which we use for amodal instance segmentation of test images.
Unseen Object Amodal Instance Segmentation via Hierarchical Occlusion Modeling
gist-ailab/uoais
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Instance-aware segmentation of unseen objects is essential for a robotic system in an unstructured environment.
WALT: Watch and Learn 2D Amodal Representation From Time-Lapse Imagery
dineshreddy91/WALT
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CVPR 2022
Labeled real data of occlusions is scarce (even in large datasets) and synthetic data leaves a domain gap, making it hard to explicitly model and learn occlusions.
AISFormer: Amodal Instance Segmentation with Transformer
AISFormer explicitly models the complex coherence between occluder, visible, amodal, and invisible masks within an object's regions of interest by treating them as learnable queries.
