Few-shot learning (FSL) has emerged as an effective learning method and shows great
potential. Despite the recent creative works in tackling FSL tasks, learning valid information …

Diffusion models have proven to be highly effective in generating high-quality images.
However, adapting large pre-trained diffusion models to new domains remains an open …

Images of the natural world collected by a variety of cameras from drones to individual
phones are increasingly abundant sources of biological information. There is an explosion …

Biodiversity monitoring using machine learning and AI-based approaches is becoming
increasingly popular. It allows for providing detailed information on species distribution and …

Abstract We introduce Meta-Album, an image classification meta-dataset designed to
facilitate few-shot learning, transfer learning, meta-learning, among other tasks. It includes …

Diffusion models have demonstrated exceptional efficacy in various generative applications.
While existing models focus on minimizing a weighted sum of denoising score matching …

Building accurate knowledge of the identity, the geographic distribution and the evolution of
species is essential for the sustainable development of humanity, as well as for biodiversity …

Biodiversity monitoring through AI approaches is essential, as it enables the efficient
analysis of vast amounts of data, providing comprehensive insights into species distribution …

Enormous waves of technological innovations over the past several years, marked by the
advances in AI technologies, are profoundly reshaping the industry and the society …

The article reviews and benchmarks machine learning methods for automatic image-based
plant species recognition and proposes a novel retrieval-based method for recognition by …