# Self-Supervised Vision Transformers Are Efficient Segmentation Learners for Imperfect Labels Source: [Self-Supervised Vision Transformers Are Efficient Segmentation Learners for Imperfect Labels]() Collection index: [llms.txt]() Data updated: 2025-10-26T02:16:25.405Z Authors: Seungho Lee, Seoungyoon Kang, Hyunjung Shim Published: 2024-01-23T07:24:16.000Z ## Abstract This study demonstrates a cost-effective approach to semantic segmentation using self-supervised vision transformers (SSVT). By freezing the SSVT backbone and training a lightweight segmentation head, our approach effectively utilizes imperfect labels, thereby improving robustness to label imperfections. Empirical experiments show significant performance improvements over existing methods for various annotation types, including scribble, point-level, and image-level labels. The research highlights the effectiveness of self-supervised vision transformers in dealing with imperfect labels, providing a practical and efficient solution for semantic segmentation while reducing annotation costs. Through extensive experiments, we confirm that our method outperforms baseline models for all types of imperfect labels. Especially under the zero-shot vision-language-model-based label, our model exhibits 11.5\\%p performance gain compared to the baseline. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()