Semantic Segmentation of Transparent and Opaque Drinking Glasses with the Help of Zero-shot Learning

• URL: http://arxiv.org/abs/2503.15004v1
• Date: Wed, 19 Mar 2025 08:54:14 GMT
• Title: Semantic Segmentation of Transparent and Opaque Drinking Glasses with the Help of Zero-shot Learning
• Authors: Annalena Blänsdorf, Tristan Wirth, Arne Rak, Thomas Pöllabauer, Volker Knauthe, Arjan Kuijper,
• Abstract summary: We propose TransCaGNet, a modified version of the zero-shot model CaGNet.<n>We use zeroshot learning to be able to create semantic segmentations of glass categories not given during training.<n>We show that TransCaGNet produces better mean IoU and accuracy values while ZegClip outperforms it mostly for unseen classes.
• Score: 4.23895653489492
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Segmenting transparent structures in images is challenging since they are difficult to distinguish from the background. Common examples are drinking glasses, which are a ubiquitous part of our lives and appear in many different shapes and sizes. In this work we propose TransCaGNet, a modified version of the zero-shot model CaGNet. We exchange the segmentation backbone with the architecture of Trans4Trans to be capable of segmenting transparent objects. Since some glasses are rarely captured, we use zeroshot learning to be able to create semantic segmentations of glass categories not given during training. We propose a novel synthetic dataset covering a diverse set of different environmental conditions. Additionally we capture a real-world evaluation dataset since most applications take place in the real world. Comparing our model with Zeg-Clip we are able to show that TransCaGNet produces better mean IoU and accuracy values while ZegClip outperforms it mostly for unseen classes. To improve the segmentation results, we combine the semantic segmentation of the models with the segmentation results of SAM 2. Our evaluation emphasizes that distinguishing between different classes is challenging for the models due to similarity, points of view, or coverings. Taking this behavior into account, we assign glasses multiple possible categories. The modification leads to an improvement up to 13.68% for the mean IoU and up to 17.88% for the mean accuracy values on the synthetic dataset. Using our difficult synthetic dataset for training, the models produce even better results on the real-world dataset. The mean IoU is improved up to 5.55% and the mean accuracy up to 5.72% on the real-world dataset.

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