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Segment scene with unknown objects without training by diffusion for reference embedding with FreeDA

Segment scene with unknown objects without training by diffusion for reference embedding with FreeDA


Training-Free Open-Vocabulary Segmentation with Offline Diffusion-Augmented Prototype Generation



Open-vocabulary semantic segmentation aims at segmenting arbitrary categories expressed in textual form.


Previous ... trained over large amounts of image-caption pairs ... However, captions ... lack localization of ... concepts ... training on large-scale datasets ... brings ... costs.


... propose FreeDA, a training-free diffusion-augmented method for open-vocabulary semantic segmentation, which leverages the ability of diffusion models to visually localize generated concepts and local-global similarities to match class-agnostic regions with semantic classes.


... approach involves an offline stage in which textual-visual reference embeddings are collected, starting from a large set of captions and leveraging visual and semantic contexts.


At test time, these are queried to support the visual matching process, which is carried out by jointly considering class-agnostic regions and global semantic similarities.


... demonstrate that FreeDA achieves state-of-the-art performance on five datasets, surpassing previous methods ... without requiring any training.



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