# Contextual Relabelling of Detected Objects

## TL;DR

This paper introduces two models that utilize contextual relationships among objects to improve the accuracy of RCNN-based object detection, demonstrating enhanced performance on the MSCOCO dataset.

## Contribution

The paper proposes rescoring and re-labeling models that incorporate 16 types of contextual relationships to boost object detection accuracy.

## Key findings

- Improved detection performance on MSCOCO dataset
- Effective use of 16 contextual relationships
- Enhancement over baseline RCNN models

## Abstract

Contextual information, such as the co-occurrence of objects and the spatial and relative size among objects provides deep and complex information about scenes. It also can play an important role in improving object detection. In this work, we present two contextual models (rescoring and re-labeling models) that leverage contextual information (16 contextual relationships are applied in this paper) to enhance the state-of-the-art RCNN-based object detection (Faster RCNN). We experimentally demonstrate that our models lead to enhancement in detection performance using the most common dataset used in this field (MSCOCO).