AHCI RESEARCH GROUP

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OUR RESEARCH

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2025

Sinha, Y.; Shanmugam, S.; Sahu, Y. K.; Mukhopadhyay, A.; Biswas, P.

Diffuse Your Data Blues: Augmenting Low-Resource Datasets via User-Assisted Diffusion Proceedings Article

In: Int Conf Intell User Interfaces Proc IUI, pp. 538–552, Association for Computing Machinery, 2025, ISBN: 979-840071306-4 (ISBN).

Abstract | Links | BibTeX | Tags: Data gathering, Detection models, Diffusion Model, diffusion models, Efficient Augmentation, Image Composition, Industrial context, Mixed reality, Object Detection, Objects detection, Synthetic Dataset, Synthetic datasets, Training objects

@inproceedings{sinha_diffuse_2025,

title = {Diffuse Your Data Blues: Augmenting Low-Resource Datasets via User-Assisted Diffusion},

author = {Y. Sinha and S. Shanmugam and Y. K. Sahu and A. Mukhopadhyay and P. Biswas},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105001924293&doi=10.1145%2f3708359.3712163&partnerID=40&md5=c13cb6b2ef757546239de8b3ba93fb14},

doi = {10.1145/3708359.3712163},

isbn = {979-840071306-4 (ISBN)},

year  = {2025},

date = {2025-01-01},

booktitle = {Int Conf Intell User Interfaces Proc IUI},

pages = {538–552},

publisher = {Association for Computing Machinery},

abstract = {Mixed reality applications in industrial contexts necessitate extensive and varied datasets for training object detection models, yet actual data gathering may be obstructed by logistical or cost issues. This study investigates the implementation of generative AI methods to work on this issue for mixed reality applications, with an emphasis on assembly and disassembly tasks. The novel objects found in industrial settings are difficult to describe using words, making text-based models less effective. In this study, a diffusion model is used to generate images by combining novel objects with various backgrounds. The backgrounds are selected where object detection in specific applications has been ineffective. This approach efficiently produces a diverse range of training samples. We compare three approaches: traditional augmentation methods, GAN-based augmentation, and Diffusion-based augmentation. Results show that the diffusion model significantly improved detection metrics. For instance, applying diffusion models to the dataset containing mechanical components of a pneumatic cylinder raised the F1 Score from 69.77 to 84.21 and the mAP@50 from 76.48 to 88.77, resulting in an increase in object detection performance, with a 67% less dataset size compared to the traditional augmented dataset. The proposed image composition diffusion model and user-friendly interface further simplify dataset enrichment, proving effective for augmenting data and improving the robustness of detection models. © 2025 Copyright held by the owner/author(s).},

keywords = {Data gathering, Detection models, Diffusion Model, diffusion models, Efficient Augmentation, Image Composition, Industrial context, Mixed reality, Object Detection, Objects detection, Synthetic Dataset, Synthetic datasets, Training objects},

pubstate = {published},

tppubtype = {inproceedings}

}