# Exploring Person Context and Local Scene Context for Object Detection

arXiv: https://arxiv.org/abs/1511.08177 · HTML: https://mlsanity.com/paper/1511.08177/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Nov 2015.

## Built on by (most cited first, 2 of 2)

- [Evaluating Context for Deep Object Detectors](https://mlsanity.com/paper/2205.02887/) (2022)
- [Measuring the Impact of Scene Level Objects on Object Detection: Towards Quantitative Explanations of Detection Decisions](https://mlsanity.com/paper/2401.10790/) (2024)

## Abstract (arXiv)

In this paper we explore two ways of using context for object detection. The first model focusses on people and the objects they commonly interact with, such as fashion and sports accessories. The second model considers more general object detection and uses the spatial relationships between objects and between objects and scenes. Our models are able to capture precise spatial relationships between the context and the object of interest, and make effective use of the appearance of the contextual region. On the newly released COCO dataset, our models provide relative improvements of up to 5% over CNN-based state-of-the-art detectors, with the gains concentrated on hard cases such as small objects (10% relative improvement).