# Diverse Generation while Maintaining Semantic Coordination: A Diffusion-Based Data Augmentation Method for Object Detection

Source: [Diverse Generation while Maintaining Semantic Coordination: A Diffusion-Based Data Augmentation Method for Object Detection](<https://www.sota2.com/research/paper/68fa745f1e209131cea613ba>)

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

Data updated: 2025-10-26T03:07:41.757Z

Authors: Sen Nie, Zhuo Wang, Xinxin Wang, Kun He

Published: 2024-08-06T01:41:40.000Z

## Abstract

Recent studies emphasize the crucial role of data augmentation in enhancing the performance of object detection models. However,existing methodologies often struggle to effectively harmonize dataset diversity with semantic coordination.To bridge this gap, we introduce an innovative augmentation technique leveraging pre-trained conditional diffusion models to mediate this balance. Our approach encompasses the development of a Category Affinity Matrix, meticulously designed to enhance dataset diversity, and a Surrounding Region Alignment strategy, which ensures the preservation of semantic coordination in the augmented images. Extensive experimental evaluations confirm the efficacy of our method in enriching dataset diversity while seamlessly maintaining semantic coordination. Our method yields substantial average improvements of +1.4AP, +0.9AP, and +3.4AP over existing alternatives on three distinct object detection models, respectively.

## 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/2408.02891>)

[PDF](<https://arxiv.org/pdf/2408.02891.pdf>)