Per-class curriculum for Unsupervised Domain Adaptation in semantic segmentation
Accurate training of deep neural networks for semantic segmentation requires a large number of pixel-level annotations of real images, which are expensive to generate or not even available. In this context, Unsupervised Domain Adaptation (UDA) can transfer knowledge from unlimited synthetic annotati...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2024 |
| País: | España |
| Institución: | Universidad Autónoma de Madrid |
| Repositorio: | Biblos-e Archivo. Repositorio Institucional de la UAM |
| Idioma: | inglés |
| OAI Identifier: | oai:repositorio.uam.es:10486/712091 |
| Acceso en línea: | http://hdl.handle.net/10486/712091 https://dx.doi.org/10.1007/s00371-024-03373-8 |
| Access Level: | acceso abierto |
| Palabra clave: | Semantic Segmentation Unsupervised Domain Adaptation Curriculum Learning Synthetic Data Informática |
| Sumario: | Accurate training of deep neural networks for semantic segmentation requires a large number of pixel-level annotations of real images, which are expensive to generate or not even available. In this context, Unsupervised Domain Adaptation (UDA) can transfer knowledge from unlimited synthetic annotations to unlabeled real images of a given domain. UDA methods are composed of an initial training stage with labeled synthetic data followed by a second stage for feature alignment between labeled synthetic and unlabeled real data. In this paper, we propose a novel approach for UDA focusing the initial training stage, which leads to increased performance after adaptation. We introduce a curriculum strategy where each semantic class is learned progressively. Thereby, better features are obtained for the second stage. This curriculum is based on: (1) a classscoring function to determine the difficulty of each semantic class, (2) a strategy for incremental learning based on scoring and pacing functions that limits the required training time unlike standard curriculum-based training and (3) a training loss to operate at class level. We extensively evaluate our approach as the first stage of several state-of-the-art UDA methods for semantic segmentation. Our results demonstrate significant performance enhancements across all methods: improvements of up to 10% for entropy-based techniques and 8% for adversarial methods. These findings underscore the dependency of UDA on the accuracy of the initial training. The implementation is available at https://github.com/vpulab/PCCL |
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