# Semantic Information for Object Detection

Source: [Semantic Information for Object Detection](<https://www.sota2.com/research/paper/68fa70561e209131cea4a574>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-28T13:57:00.578Z

Authors: Jean-Francois Nies

Published: 2023-08-17T13:53:29.000Z

## Abstract

In this paper, we demonstrate that the concept of Semantic Consistency and the ensuing method of Knowledge-Aware Re-Optimization can be adapted for the problem of object detection in intricate traffic scenes. Furthermore, we introduce a novel method for extracting a knowledge graph from a dataset of images provided with instance-level annotations, and integrate this new knowledge graph with the existing semantic consistency model. Combining both this novel hybrid knowledge graph and the preexisting methods of frequency analysis and external knowledge graph as sources for semantic information, we investigate the effectiveness of knowledge-aware re-optimization on the Faster-RCNN and DETR object detection models. We find that limited but consistent improvements in precision and or recall can be achieved using this method for all combinations of model and method studied.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2308.08990>)

[PDF](<https://arxiv.org/pdf/2308.08990.pdf>)