# Sequential Amodal Segmentation via Cumulative Occlusion Learning

arXiv: https://arxiv.org/abs/2405.05791 · HTML: https://mlsanity.com/paper/2405.05791/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 41 references that are in the map.
- First posted on arXiv 9 May 2024.

## Builds on (most cited first, 30 of 32)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Improved Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2102.09672/) (2021)
- [DiffEdit: Diffusion-based semantic image editing with mask guidance](https://mlsanity.com/paper/2210.11427/) (2022)
- [PointRend: Image Segmentation as Rendering](https://mlsanity.com/paper/1912.08193/) (2019)
- [Open-Vocabulary Panoptic Segmentation with Text-to-Image Diffusion Models](https://mlsanity.com/paper/2303.04803/) (2023)
- [Diffusion Models for Implicit Image Segmentation Ensembles](https://mlsanity.com/paper/2112.03145/) (2021)
- [MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic Model](https://mlsanity.com/paper/2211.00611/) (2022)
- [CascadePSP: Toward Class-Agnostic and Very High-Resolution Segmentation via Global and Local Refinement](https://mlsanity.com/paper/2005.02551/) (2020)
- [Deep Occlusion-Aware Instance Segmentation with Overlapping BiLayers](https://mlsanity.com/paper/2103.12340/) (2021)
- [Semantic Amodal Segmentation](https://mlsanity.com/paper/1509.01329/) (2015)
- [Self-Supervised Scene De-occlusion](https://mlsanity.com/paper/2004.02788/) (2020)
- [Ambiguous Medical Image Segmentation using Diffusion Models](https://mlsanity.com/paper/2304.04745/) (2023)
- [Open-vocabulary Object Segmentation with Diffusion Models](https://mlsanity.com/paper/2301.05221/) (2023)
- [Learning to See the Invisible: End-to-End Trainable Amodal Instance Segmentation](https://mlsanity.com/paper/1804.08864/) (2018)
- [SeGAN: Segmenting and Generating the Invisible](https://mlsanity.com/paper/1703.10239/) (2017)
- [Unseen Object Amodal Instance Segmentation via Hierarchical Occlusion Modeling](https://mlsanity.com/paper/2109.11103/) (2021)
- [Amodal Panoptic Segmentation](https://mlsanity.com/paper/2202.11542/) (2022)
- [Stochastic Segmentation with Conditional Categorical Diffusion Models](https://mlsanity.com/paper/2303.08888/) (2023)
- [AISFormer: Amodal Instance Segmentation with Transformer](https://mlsanity.com/paper/2210.06323/) (2022)
- [Instance-wise Occlusion and Depth Orders in Natural Scenes](https://mlsanity.com/paper/2111.14562/) (2021)
- [Amodal Segmentation through Out-of-Task and Out-of-Distribution Generalization with a Bayesian Model](https://mlsanity.com/paper/2010.13175/) (2020)
- [Coarse-to-Fine Amodal Segmentation with Shape Prior](https://mlsanity.com/paper/2308.16825/) (2023)
- [Robust Instance Segmentation through Reasoning about Multi-Object Occlusion](https://mlsanity.com/paper/2012.02107/) (2020)
- [Visiting the Invisible: Layer-by-Layer Completed Scene Decomposition](https://mlsanity.com/paper/2104.05367/) (2021)
- [Video Class Agnostic Segmentation Benchmark for Autonomous Driving](https://mlsanity.com/paper/2103.11015/) (2021)
- [A Weakly Supervised Amodal Segmenter with Boundary Uncertainty Estimation](https://mlsanity.com/paper/2108.09897/) (2021)
- [Modeling Multimodal Aleatoric Uncertainty in Segmentation with Mixture of Stochastic Experts](https://mlsanity.com/paper/2212.07328/) (2022)
- [Joint Learning of Instance and Semantic Segmentation for Robotic Pick-and-Place with Heavy Occlusions in Clutter](https://mlsanity.com/paper/2001.07481/) (2020)

## Built on by (most cited first, 1 of 1)

- [QCell: Recombining and Aligning Cell Queries for Overlapping Instance Segmentation](https://mlsanity.com/paper/2608.29253/) (2026)

## Abstract (arXiv)

To fully understand the 3D context of a single image, a visual system must be able to segment both the visible and occluded regions of objects, while discerning their occlusion order. Ideally, the system should be able to handle any object and not be restricted to segmenting a limited set of object classes, especially in robotic applications. Addressing this need, we introduce a diffusion model with cumulative occlusion learning designed for sequential amodal segmentation of objects with uncertain categories. This model iteratively refines the prediction using the cumulative mask strategy during diffusion, effectively capturing the uncertainty of invisible regions and adeptly reproducing the complex distribution of shapes and occlusion orders of occluded objects. It is akin to the human capability for amodal perception, i.e., to decipher the spatial ordering among objects and accurately predict complete contours for occluded objects in densely layered visual scenes. Experimental results across three amodal datasets show that our method outperforms established baselines.