Vision–Language-Guided Pseudo-Labels for Unsupervised Domain Adaptation in Semantic Segmentation for Waste Sorting
MCML Authors
Shubhangi Shubhangi
Thomas Seidl
Prof. Dr.
Director
Abstract
Shubhangi Shubhangi
Thomas Seidl
Prof. Dr.
Director
Abstract
Obtaining labeled data for semantic segmentation in applied settings (e.g., autonomous driving, industrial waste sorting) is expensive and often infeasible at scale. We present a cross-modal pseudo-labeling pipeline that enables unsupervised domain adaptation without any target-domain annotations. The pipeline is built on two core foundation models: SAM generates class-agnostic region proposals, and EVA-CLIP assigns semantic labels based on region–text similarity, with confidence filtering ensuring that only reliable pseudo-labels are used for self-training a segmentation model. As an optional extension, BLIP provides language-grounded verification for ambiguous regions, thereby improving pseudo-label quality without altering the overall pipeline. Evaluated on two domain shifts, synthetic-to-real autonomous driving and, with a primary focus, lab-to-factory industrial waste sorting, the pipeline consistently improves over source-only baselines. Our results demonstrate that pseudo-label quality, not quantity, is a decisive factor in self-training under domain shift, and that cross-modal language grounding offers a practical path to reliable automatic annotation in deployment-critical applications.
inproceedings SSD+26b
ECML-PKDD 2026
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases. Naples, Italy, Sep 07-11, 2026. To be published.
Authors
U. Schlegel • S. Shubhangi • G. Dax • S. R. Kaminwar • F. Karl • T. SeidlResearch Areas
BibTeXKey: SSD+26b