A Generative Approach at the Instance-Level for Image Segmentation Under Limited Training Data Conditions (Student Abstract)

Thanh-Danh Nguyen, Vinh-Tiep Nguyen, Tam V. Nguyen

AAAI 2025 pp. 29451-29452

doi:10.1609/AAAI.V39I28.35284 /aaai/2025/nguyen2025aaai-generative/

Abstract

High-accuracy image segmentation models require abundant training annotated data which is costly for pixel-level annotations. Our work addresses a high-cost manual annotating process or the lack of detailed annotations via a generative approach. In particular, our approach (1) proposes the conditional instance-level synthesis to enrich the limited data to enhance the segmentation performance, and (2) employs the generative architectures to complete the segmentation task under few-shot learning concepts. The initial results on the Cityscapes benchmark emphasize our potential generative solution on the instance segmentation task given limited data.

AAAI Semantic Scholar

Cite

Text

Nguyen et al. "A Generative Approach at the Instance-Level for Image Segmentation Under Limited Training Data Conditions (Student Abstract)." AAAI Conference on Artificial Intelligence, 2025. doi:10.1609/AAAI.V39I28.35284

Markdown

[Nguyen et al. "A Generative Approach at the Instance-Level for Image Segmentation Under Limited Training Data Conditions (Student Abstract)." AAAI Conference on Artificial Intelligence, 2025.](https://mlanthology.org/aaai/2025/nguyen2025aaai-generative/) doi:10.1609/AAAI.V39I28.35284

BibTeX

@inproceedings{nguyen2025aaai-generative,
  title     = {{A Generative Approach at the Instance-Level for Image Segmentation Under Limited Training Data Conditions (Student Abstract)}},
  author    = {Nguyen, Thanh-Danh and Nguyen, Vinh-Tiep and Nguyen, Tam V.},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2025},
  pages     = {29451-29452},
  doi       = {10.1609/AAAI.V39I28.35284},
  url       = {https://mlanthology.org/aaai/2025/nguyen2025aaai-generative/}
}