Semi-Supervised Domain Generalization for Object Detection via Language-Guided Feature Alignment
Sina Malakouti (University of Pittsburgh),* Adriana Kovashka (University of Pittsburgh)
The 34th British Machine Vision Conference
Abstract
Existing domain adaptation (DA) and generalization (DG) methods in object detection enforce feature alignment in the visual space but face challenges like object appearance variability and scene complexity which make it difficult to distinguish between objects and achieve accurate detection. In this paper, we are the first to address the problem of semi-supervised domain generalization by exploring vision-language pre-training and enforcing feature alignment through the language space. We employ a novel Cross-Domain Descriptive Multi-Scale Learning (CDDMSL) aiming to maximize the agreement between descriptions of an image presented with different domain-specific characteristics in the embedding space. CDDMSL significantly outperforms existing methods, achieving $11.7\%$ and $7.5\%$ improvement in DG and DA settings, respectively. Comprehensive analysis and ablation studies confirm the effectiveness of our method, positioning CDDMSL as a promising approach for domain generalization in object detection tasks.Video
Citation
@inproceedings{Malakouti_2023_BMVC,
author = {Sina Malakouti and Adriana Kovashka},
title = {Semi-Supervised Domain Generalization for Object Detection via Language-Guided Feature Alignment},
booktitle = {34th British Machine Vision Conference 2023, {BMVC} 2023, Aberdeen, UK, November 20-24, 2023},
publisher = {BMVA},
year = {2023},
url = {https://papers.bmvc2023.org/0444.pdf}
}
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