Semantic Library Adaptation: A New Approach for Open-Vocabulary Semantic Segmentation

Open-Vocabulary Semantic Segmentation (OVSS) allows the assignment of pixels in images to an unlimited number of classes based on textual queries. This technology combines image and text information to analyze objects and scenes in detail. OVSS models show promising results on new datasets, but their performance is affected by large differences between training and test data. Therefore, fine-tuning is often required for effective practical use.

A new approach to solving this problem is called Semantic Library Adaptation (SemLA). SemLA is a framework for training-free domain adaptation at test time. In contrast to conventional methods that require retraining the model, SemLA allows adaptation to new domains without additional training effort.

How SemLA Works

SemLA uses a library of LoRA-based adapters indexed with CLIP embeddings. LoRA (Low-Rank Adaptation) is an efficient technique for fine-tuning large language models, which is applied here to image processing. CLIP (Contrastive Language-Image Pre-training) is used to generate semantic embeddings that describe the relationship between images and text.

The core of SemLA lies in the dynamic selection and combination of relevant adapters. Based on the proximity to the target domain in the embedding space, the most suitable adapters are selected and merged. This creates an ad-hoc model specifically tailored to the respective input, without the need for retraining.

Advantages of SemLA

SemLA offers several advantages over conventional domain adaptation methods:

Scalability: The use of LoRA adapters allows efficient scaling to large datasets and complex scenarios.

Explainability: By tracking the adapter contributions, the model's decision-making process can be understood.

Data Privacy: Since no retraining with the target domain data is required, data privacy is ensured. This is particularly important for sensitive applications.

Evaluation and Results

The effectiveness of SemLA has been demonstrated in comprehensive experiments on a benchmark with 20 domains, based on 10 standard datasets. The results show that SemLA offers superior adaptability and performance in various environments and sets a new standard in domain adaptation for Open-Vocabulary Semantic Segmentation.

Outlook

SemLA represents a promising approach for domain adaptation in Open-Vocabulary Semantic Segmentation. The training-free adaptation to new domains, scalability, and data privacy friendliness make SemLA an attractive solution for various applications. Future research could focus on expanding the adapter library and optimizing the selection and fusion mechanisms.

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