# Seeing What Is Not There: Learning Context to Determine Where Objects Are Missing

arXiv: https://arxiv.org/abs/1702.07971 · HTML: https://mlsanity.com/paper/1702.07971/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Feb 2017.

## Built on by (most cited first, 11 of 11)

- [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)
- [Efficient Zero-shot Visual Search via Target and Context-aware Transformer](https://mlsanity.com/paper/2211.13470/) (2022)
- [Common Inpainted Objects In-N-Out of Context](https://mlsanity.com/paper/2506.00721/) (2025)
- [Evaluating Context for Deep Object Detectors](https://mlsanity.com/paper/2205.02887/) (2022)
- [Detecting out-of-context objects using contextual cues](https://mlsanity.com/paper/2202.05930/) (2022)
- [GMC: A General Framework of Multi-stage Context Learning and Utilization for Visual Detection Tasks](https://mlsanity.com/paper/2407.05566/) (2024)
- [CueCAn: Cue Driven Contextual Attention For Identifying Missing Traffic Signs on Unconstrained Roads](https://mlsanity.com/paper/2303.02641/) (2023)
- [Learning Scene Context Without Images](https://mlsanity.com/paper/2311.10998/) (2023)
- [Improving Classification of Occluded Objects through Scene Context](https://mlsanity.com/paper/2510.26681/) (2025)
- [Knowledge-Guided Failure Prediction: Detecting When Object Detectors Miss Safety-Critical Objects](https://mlsanity.com/paper/2603.25499/) (2026)

## Abstract (arXiv)

Most of computer vision focuses on what is in an image. We propose to train a standalone object-centric context representation to perform the opposite task: seeing what is not there. Given an image, our context model can predict where objects should exist, even when no object instances are present. Combined with object detection results, we can perform a novel vision task: finding where objects are missing in an image. Our model is based on a convolutional neural network structure. With a specially designed training strategy, the model learns to ignore objects and focus on context only. It is fully convolutional thus highly efficient. Experiments show the effectiveness of the proposed approach in one important accessibility task: finding city street regions where curb ramps are missing, which could help millions of people with mobility disabilities.