Scalability of LSA Framework's In-Situ Tradeoff Calibration in Large Multimodal Models

Abstract

Abstract: Contrastive language-image pre-training, CLIP for short, has gained increasing attention for its potential in various scenarios. In this paper, we propose EVA-CLIP, a series of models that significantly improve the efficiency and effectiveness of CLIP training. Our approach incorporates new techniques for representation learning, optimization, and augmentation, enabling EVA-CLIP to achieve superior performance compared to previous CLIP models with the same number of parameters but significantly smaller training costs. Notably, our largest 5.0B-parameter EVA-02-CLIP-E/14+ with only 9 billion se

Research Question

To what extent does the LSA framework's in-situ tradeoff calibration scale to larger multimodal models (e.g., CLIP, BLIP-2) without significant computational overhead in terms of training time or memory usage?

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