# MT-ORL: Multi-Task Occlusion Relationship Learning

arXiv: https://arxiv.org/abs/2108.05722 · HTML: https://mlsanity.com/paper/2108.05722/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 38 references that are in the map.
- First posted on arXiv 12 Aug 2021.

## Builds on (most cited first, 17 of 17)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [EfficientDet: Scalable and Efficient Object Detection](https://mlsanity.com/paper/1911.09070/) (2019)
- [Holistically-Nested Edge Detection](https://mlsanity.com/paper/1504.06375/) (2015)
- [Bag of Tricks for Image Classification with Convolutional Neural Networks](https://mlsanity.com/paper/1812.01187/) (2018)
- [Multi-scale Interactive Network for Salient Object Detection](https://mlsanity.com/paper/2007.09062/) (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)
- [SharpNet: Fast and Accurate Recovery of Occluding Contours in Monocular Depth Estimation](https://mlsanity.com/paper/1905.08598/) (2019)
- [Predicting Sharp and Accurate Occlusion Boundaries in Monocular Depth Estimation Using Displacement Fields](https://mlsanity.com/paper/2002.12730/) (2020)
- [Occlusion-shared and Feature-separated Network for Occlusion Relationship Reasoning](https://mlsanity.com/paper/1908.05898/) (2019)
- [Monocular Object Instance Segmentation and Depth Ordering with CNNs](https://mlsanity.com/paper/1505.03159/) (2015)
- [Affinity CNN: Learning Pixel-Centric Pairwise Relations for Figure/Ground Embedding](https://mlsanity.com/paper/1512.02767/) (2015)
- [Pixel-Pair Occlusion Relationship Map(P2ORM): Formulation, Inference & Application](https://mlsanity.com/paper/2007.12088/) (2020)
- [DOOBNet: Deep Object Occlusion Boundary Detection from an Image](https://mlsanity.com/paper/1806.03772/) (2018)
- [DOC: Deep OCclusion Estimation From a Single Image](https://mlsanity.com/paper/1511.06457/) (2015)
- [Context-Constrained Accurate Contour Extraction for Occlusion Edge Detection](https://mlsanity.com/paper/1903.08890/) (2019)

## Built on by (most cited first, 4 of 4)

- [Interactive Occlusion Boundary Estimation through Exploitation of Synthetic Data](https://mlsanity.com/paper/2408.15038/) (2024)
- [Occlusion Boundary and Depth: Mutual Enhancement via Multi-Task Learning](https://mlsanity.com/paper/2505.21231/) (2025)
- [Detection Selection Algorithm: A Likelihood based Optimization Method to Perform Post Processing for Object Detection](https://mlsanity.com/paper/2212.05706/) (2022)
- [RealOOB: A Definition-Consistent Real-World Oriented Occlusion Boundary Benchmark](https://mlsanity.com/paper/2608.30820/) (2026)

## Abstract (arXiv)

Retrieving occlusion relation among objects in a single image is challenging due to sparsity of boundaries in image. We observe two key issues in existing works: firstly, lack of an architecture which can exploit the limited amount of coupling in the decoder stage between the two subtasks, namely occlusion boundary extraction and occlusion orientation prediction, and secondly, improper representation of occlusion orientation. In this paper, we propose a novel architecture called Occlusion-shared and Path-separated Network (OPNet), which solves the first issue by exploiting rich occlusion cues in shared high-level features and structured spatial information in task-specific low-level features. We then design a simple but effective orthogonal occlusion representation (OOR) to tackle the second issue. Our method surpasses the state-of-the-art methods by 6.1%/8.3% Boundary-AP and 6.5%/10% Orientation-AP on standard PIOD/BSDS ownership datasets. Code is available at https://github.com/fengpanhe/MT-ORL.