Abstract Pretrained Foundation Models (PFMs) are regarded as the foundation for various
downstream tasks across different data modalities. A PFM (eg, BERT, ChatGPT, GPT-4) is …

ABSTRACT A common need for artificial intelligence models in the broader geoscience is to
encode various types of spatial data, such as points, polylines, polygons, graphs, or rasters …

Most visual recognition studies rely heavily on crowd-labelled data in deep neural networks
(DNNs) training, and they usually train a DNN for each single visual recognition task …

We launch EVA-02, a next-generation Transformer-based visual representation pre-trained
to reconstruct strong and robust language-aligned vision features via masked image …

The exponential growth of large language models (LLMs) has opened up numerous
possibilities for multi-modal AGI systems. However the progress in vision and vision …

Open-vocabulary models are a promising new paradigm for image classification. Unlike
traditional classification models, open-vocabulary models classify among any arbitrary set of …

Automated visual understanding of our diverse and open world demands computer vision
models to generalize well with minimal customization for specific tasks, similar to human …

Recent text-to-image generation models have shown promising results in generating high-
fidelity photo-realistic images. Though the results are astonishing to human eyes, how …

Self-supervised learning algorithms based on instance discrimination train encoders to be
invariant to pre-defined transformations of the same instance. While most methods treat …

Fine-grained image analysis (FGIA) is a longstanding and fundamental problem in computer
vision and pattern recognition, and underpins a diverse set of real-world applications. The …