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VeCAF: Efficient Vision-Language Finetuning by 720matheusmendes is a document available to read on EtoBox.

What is VeCAF: Efficient Vision-Language Finetuning about?

The document presents VeCAF, a novel framework for finetuning pretrained vision models (PVMs) that enhances training efficiency by using vision-language collaboration and objective-aware data selection. By leveraging large-scale labeled datasets and natural language annotations, VeCAF selects significant data points that accelerate convergence and improve performance, achieving up to 3.3 times less training batches on ImageNet compared to traditional methods. Extensive experiments demonstrate VeCAF

Author

720matheusmendes

Language

EN