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The current modus operandi in adapting pre-trained models involves updating all the backbone parameters, ie, full fine-tuning. This paper introduces Visual Prompt Tuning (VPT) as an efficient and effective alternative to full fine-tuning for large-scale Transformer models in vision. Taking inspiration from recent advances in efficiently tuning large language models, VPT introduces only a small amount (less than 1% of model parameters) of trainable parameters in the input space while keeping the model backbone frozen. Via extensive experiments on a wide variety of downstream recognition tasks, we show that VPT achieves significant performance gains compared to other parameter efficient tuning protocols. Most importantly, VPT even outperforms full fine-tuning in many cases across model capacities and training data scales, while reducing per-task storage cost. 

Herbarium sheets present a unique view of the world's botanical history, evolution, and diversity. This makes them an all-important data source for botanical research. With the increased digitisation of herbaria worldwide and the advances in the fine-grained classification domain that can facilitate automatic identification of herbarium specimens, there are a lot of opportunities for supporting research in this field. However, existing datasets are either too small, or not diverse enough, in terms of represented taxa, geographic distribution or host institutions. Furthermore, aggregating multiple datasets is difficult as taxa exist under a multitude of different names and the taxonomy requires alignment to a common reference. We present the Herbarium Half-Earth dataset, the largest and most diverse dataset of herbarium specimens to date for automatic taxon recognition. https://doi.org/10.48550/arXiv.2105.13808 

Herbarium sheets present a unique view of the world's botanical history, evolution, and biodiversity. This makes them an all–important data source for botanical research. With the increased digitization of herbaria worldwide and advances in the domain of fine–grained visual classification which can facilitate automatic identification of herbarium specimen images, there are many opportunities for supporting and expanding research in this field. However, existing datasets are either too small, or not diverse enough, in terms of represented taxa, geographic distribution, and imaging protocols. Furthermore, aggregating datasets is difficult as taxa are recognized under a multitude of names and must be aligned to a common reference. We introduce the Herbarium 2021 Half–Earth dataset: the largest and most diverse dataset of herbarium specimen images, to date, for automatic taxon recognition. We also present the results of the Herbarium 2021 Half–Earth challenge, a competition that was part of the Eighth Workshop on Fine-Grained Visual Categorization (FGVC8) and hosted by Kaggle to encourage the development of models to automatically identify taxa from herbarium sheet images.