Navigating the Landscape of CLIP: Investigating Data, Architecture, and Training Strategies

Researchers have recently seen a surge of interest in image-and-language representation learning, aiming to capture the intricate relationship between visual and textual information. Among all the Contrastive Language-Image Pre-Training (CLIP) frameworks, it has emerged as a promising approach, demonstrating state-of-the-art performance across various tasks and robustness to out-of-distribution data. While previous studies focused on scaling […]

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