# Improving Classification of Occluded Objects through Scene Context

arXiv: https://arxiv.org/abs/2510.26681 · HTML: https://mlsanity.com/paper/2510.26681/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 32 references that are in the map.
- First posted on arXiv 30 Oct 2025.

## Builds on (most cited first, 19 of 19)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [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)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [Pose2Seg: Detection Free Human Instance Segmentation](https://mlsanity.com/paper/1803.10683/) (2018)
- [Robust Object Detection under Occlusion with Context-Aware CompositionalNets](https://mlsanity.com/paper/2005.11643/) (2020)
- [Compositional Convolutional Neural Networks: A Deep Architecture with Innate Robustness to Partial Occlusion](https://mlsanity.com/paper/2003.04490/) (2020)
- [Context R-CNN: Long Term Temporal Context for Per-Camera Object Detection](https://mlsanity.com/paper/1912.03538/) (2019)
- [TACO: Trash Annotations in Context for Litter Detection](https://mlsanity.com/paper/2003.06975/) (2020)
- [Compositional Convolutional Neural Networks: A Robust and Interpretable Model for Object Recognition under Occlusion](https://mlsanity.com/paper/2006.15538/) (2020)
- [When Pigs Fly: Contextual Reasoning in Synthetic and Natural Scenes](https://mlsanity.com/paper/2104.02215/) (2021)
- [Context Understanding in Computer Vision: A Survey](https://mlsanity.com/paper/2302.05011/) (2023)
- [A Tri-Layer Plugin to Improve Occluded Detection](https://mlsanity.com/paper/2210.10046/) (2022)
- [Seeing What Is Not There: Learning Context to Determine Where Objects Are Missing](https://mlsanity.com/paper/1702.07971/) (2017)
- [Are Deep Learning Models Robust to Partial Object Occlusion in Visual Recognition Tasks?](https://mlsanity.com/paper/2409.10775/) (2024)
- [GMC: A General Framework of Multi-stage Context Learning and Utilization for Visual Detection Tasks](https://mlsanity.com/paper/2407.05566/) (2024)

## Abstract (arXiv)

The presence of occlusions has provided substantial challenges to typically-powerful object recognition algorithms. Additional sources of information can be extremely valuable to reduce errors caused by occlusions. Scene context is known to aid in object recognition in biological vision. In this work, we attempt to add robustness into existing Region Proposal Network-Deep Convolutional Neural Network (RPN-DCNN) object detection networks through two distinct scene-based information fusion techniques. We present one algorithm under each methodology: the first operates prior to prediction, selecting a custom object network to use based on the identified background scene, and the second operates after detection, fusing scene knowledge into initial object scores output by the RPN. We demonstrate our algorithms on challenging datasets featuring partial occlusions, which show overall improvement in both recall and precision against baseline methods. In addition, our experiments contrast multiple training methodologies for occlusion handling, finding that training on a combination of both occluded and unoccluded images demonstrates an improvement over the others. Our method is interpretable and can easily be adapted to other datasets, offering many future directions for research and practical applications.