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
Latest papers
Amodal Instance Segmentation With KINS Dataset
qqlu/Amodal-Instance-Segmentation-through-KINS-Dataset
•
•
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.
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.
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.
