arXiv:2602.21397v2 Announce Type: replace-cross Abstract: Prompt learning has become a dominant paradigm for adapting vision-language models (VLMs) such as CLIP to downstream tasks without modifying pretrained weights. While extending prompts to both vision and text encoders across multiple transformer layers significantly boosts performance, it dramatically increases the number of trainable parameters, with state-of-the-art methods requiring millions of parameters and abandoning the parameter efficiency that makes prompt tuning attractive. In this work, we propose MMLoP (Multi-Modal Low-Rank
The continuous growth in VLM model size and computational demands is driving innovation in parameter-efficient fine-tuning techniques.
Improving the efficiency of adapting large vision-language models allows broader access, reduces computational costs, and accelerates AI research and deployment.
Prompt learning for VLMs becomes significantly more parameter-efficient, potentially making sophisticated AI models more accessible to developers and smaller organizations.
- · AI researchers
- · Smaller AI development teams
- · Cloud computing providers
- · Open-source AI
- · Companies reliant on prohibitive compute costs as a competitive moat
Reduced computational overhead for adapting large pre-trained vision-language models like CLIP to specific tasks.
Increased adoption and democratization of powerful VLMs across various industries due to lower resource requirements.
Acceleration of AI agent development and deployment as vision-language understanding becomes cheaper and more adaptable.
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Read at arXiv cs.LG