# GPT-4 for Occlusion Order Recovery

arXiv: https://arxiv.org/abs/2509.22383 · HTML: https://mlsanity.com/paper/2509.22383/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 27 references that are in the map.
- First posted on arXiv 26 Sep 2025.

## Builds on (most cited first, 18 of 18)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models](https://mlsanity.com/paper/2303.04671/) (2023)
- [VisionLLM: Large Language Model is also an Open-Ended Decoder for Vision-Centric Tasks](https://mlsanity.com/paper/2305.11175/) (2023)
- [Semantic Amodal Segmentation](https://mlsanity.com/paper/1509.01329/) (2015)
- [Self-Supervised Scene De-occlusion](https://mlsanity.com/paper/2004.02788/) (2020)
- [SeGAN: Segmenting and Generating the Invisible](https://mlsanity.com/paper/1703.10239/) (2017)
- [Amodal Ground Truth and Completion in the Wild](https://mlsanity.com/paper/2312.17247/) (2023)
- [Amodal Completion via Progressive Mixed Context Diffusion](https://mlsanity.com/paper/2312.15540/) (2023)
- [Instance-wise Occlusion and Depth Orders in Natural Scenes](https://mlsanity.com/paper/2111.14562/) (2021)
- [Occlusion Handling in Generic Object Detection: A Review](https://mlsanity.com/paper/2101.08845/) (2021)
- [Open-World Amodal Appearance Completion](https://mlsanity.com/paper/2411.13019/) (2024)
- [Image Amodal Completion: A Survey](https://mlsanity.com/paper/2207.02062/) (2022)
- [Monocular Object Instance Segmentation and Depth Ordering with CNNs](https://mlsanity.com/paper/1505.03159/) (2015)
- [Distance-Aware Occlusion Detection with Focused Attention](https://mlsanity.com/paper/2208.11122/) (2022)
- [BBBD: Bounding Box Based Detector for Occlusion Detection and Order Recovery](https://mlsanity.com/paper/2204.12841/) (2022)

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

- [Object Counting with GPT-4o and GPT-5: A Comparative Study](https://mlsanity.com/paper/2512.03233/) (2025)

## Abstract (arXiv)

Occlusion remains a significant challenge for current vision models to robustly interpret complex and dense real-world images and scenes. To address this limitation and to enable accurate prediction of the occlusion order relationship between objects, we propose leveraging the advanced capability of a pre-trained GPT-4 model to deduce the order. By providing a specifically designed prompt along with the input image, GPT-4 can analyze the image and generate order predictions. The response can then be parsed to construct an occlusion matrix which can be utilized in assisting with other occlusion handling tasks and image understanding. We report the results of evaluating the model on COCOA and InstaOrder datasets. The results show that by using semantic context, visual patterns, and commonsense knowledge, the model can produce more accurate order predictions. Unlike baseline methods, the model can reason about occlusion relationships in a zero-shot fashion, which requires no annotated training data and can easily be integrated into occlusion handling frameworks.