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Preprints, Working Papers, ... Year : 2023

A Simple Recipe for Language-guided Domain Generalized Segmentation


Generalization to new domains not seen during training is one of the long-standing goals and challenges in deploying neural networks in real-world applications. Existing generalization techniques necessitate substantial data augmentation, potentially sourced from external datasets, and aim at learning invariant representations by imposing various alignment constraints. Large-scale pretraining has recently shown promising generalization capabilities, along with the potential of bridging different modalities. For instance, the recent advent of vision-language models like CLIP has opened the doorway for vision models to exploit the textual modality. In this paper, we introduce a simple framework for generalizing semantic segmentation networks by employing language as the source of randomization. Our recipe comprises three key ingredients: i) the preservation of the intrinsic CLIP robustness through minimal fine-tuning, ii) language-driven local style augmentation, and iii) randomization by locally mixing the source and augmented styles during training. Extensive experiments report state-of-the-art results on various generalization benchmarks. The code will be made available.

Dates and versions

hal-04366798 , version 1 (29-12-2023)



Mohammad Fahes, Tuan-Hung Vu, Andrei Bursuc, Patrick Pérez, Raoul de Charette. A Simple Recipe for Language-guided Domain Generalized Segmentation. 2023. ⟨hal-04366798⟩


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