MT-ORL: Multi-Task Occlusion Relationship Learning

Panhe Feng, Qi She, Lei Zhu, Jiaxin Li, Lin Zhang, Zijian Feng, Changhu Wang, Chunpeng Li, Xuejing Kang, Anlong Ming; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 9364-9373


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

Related Material

@InProceedings{Feng_2021_ICCV, author = {Feng, Panhe and She, Qi and Zhu, Lei and Li, Jiaxin and Zhang, Lin and Feng, Zijian and Wang, Changhu and Li, Chunpeng and Kang, Xuejing and Ming, Anlong}, title = {MT-ORL: Multi-Task Occlusion Relationship Learning}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, month = {October}, year = {2021}, pages = {9364-9373} }