arXiv:2410.21361v2 Announce Type: replace-cross Abstract: Domain adaptation has been extensively investigated in computer vision but still requires access to target data at the training time, which might be difficult to obtain in real-world autonomous driving scenarios, especially under rare or adverse conditions. In this paper, we present a new framework for domain adaptation relying on a single Vision-Language (VL) latent embedding instead of full target data. First, leveraging a contrastive language-image pre-training model (CLIP), we propose prompt/photo-driven instance normalization (PIN)
The proliferation of real-world AI applications, especially in domains like autonomous driving, necessitates more robust and adaptable domain adaptation techniques to overcome data scarcity and deployment challenges.
This paper presents a novel approach to domain adaptation that reduces reliance on extensive target data, potentially accelerating the deployment of AI systems in complex, data-poor environments.
Traditional domain adaptation methods often require significant target data during training; this framework shifts towards leveraging single vision-language embeddings, simplifying the adaptation process.
- · Autonomous driving companies
- · AI developers in niche domains
- · Robotics
- · Computer vision research
- · Companies reliant on large, diverse target datasets for adaptation
- · Traditional domain adaptation methodologies
AI models become more adaptable and deployable across diverse real-world conditions without extensive retraining.
This could lead to a faster pace of AI adoption in industries where data collection for every scenario is prohibitive.
The reduced barrier to deployment might accelerate the development of specialized AI agents for hazardous or unique environments.
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