# Context-Aware Automatic Occlusion Removal

arXiv: https://arxiv.org/abs/1905.02710 · HTML: https://mlsanity.com/paper/1905.02710/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 May 2019.

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

- [End-To-End Data-Dependent Routing in Multi-Path Neural Networks](https://mlsanity.com/paper/2107.02450/) (2021)

## Abstract (arXiv)

Occlusion removal is an interesting application of image enhancement, for which, existing work suggests manually-annotated or domain-specific occlusion removal. No work tries to address automatic occlusion detection and removal as a context-aware generic problem. In this paper, we present a novel methodology to identify objects that do not relate to the image context as occlusions and remove them, reconstructing the space occupied coherently. The proposed system detects occlusions by considering the relation between foreground and background object classes represented as vector embeddings, and removes them through inpainting. We test our system on COCO-Stuff dataset and conduct a user study to establish a baseline in context-aware automatic occlusion removal.