Amodal Instance Segmentation

7 papers with code • 0 benchmarks • 0 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


Use these libraries to find Amodal Instance Segmentation models and implementations

Most implemented papers

Learning to See the Invisible: End-to-End Trainable Amodal Instance Segmentation

apchenstu/SLN-Amodal 24 Apr 2018

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

apchenstu/SLN-Amodal 30 May 2019

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 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

yanfengliu/layered_embeddings 14 Feb 2020

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 25 Oct 2020

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 CVPR 2021

Segmenting highly-overlapping objects is challenging, because typically no distinction is made between real object contours and occlusion boundaries.

Unseen Object Amodal Instance Segmentation via Hierarchical Occlusion Modeling

gist-ailab/uoais 23 Sep 2021

Instance-aware segmentation of unseen objects is essential for a robotic system in an unstructured environment.