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cs.CV2026
Vision-Language-Guided Pseudo-Labels for Unsupervised Domain Adaptation in Semantic Segmentation for Waste Sorting
Udo Schlegel, Shubhangi, Gabriel Dax +3
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 c…
cs.CV2025
Human Uncertainty-Aware Data Selection and Automatic Labeling in Visual Question Answering
Jian Lan, Zhicheng Liu, Udo Schlegel +5
Large vision-language models (VLMs) achieve strong performance in Visual Question Answering but still rely heavily on supervised fine-tuning (SFT) with massive labeled datasets, wh…