Amodal Instance Segmentation

13 papers with code • 1 benchmarks • 2 datasets

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

Description Credit: Amodal Instance Segmentation, ECCV 2016


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 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 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 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 23 Sep 2021

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 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

uark-aicv/aisformer 12 Oct 2022

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.