A comprehensive survey on pretrained foundation models: A history from bert to chatgpt

C Zhou, Q Li, C Li, J Yu, Y Liu, G Wang… - arXiv preprint arXiv …, 2023 - arxiv.org
Pretrained Foundation Models (PFMs) are regarded as the foundation for various
downstream tasks with different data modalities. A PFM (eg, BERT, ChatGPT, and GPT-4) is …

Self-supervised learning for medical image classification: a systematic review and implementation guidelines

SC Huang, A Pareek, M Jensen, MP Lungren… - NPJ Digital …, 2023 - nature.com
Advancements in deep learning and computer vision provide promising solutions for
medical image analysis, potentially improving healthcare and patient outcomes. However …

Dinov2: Learning robust visual features without supervision

M Oquab, T Darcet, T Moutakanni, H Vo… - arXiv preprint arXiv …, 2023 - arxiv.org
The recent breakthroughs in natural language processing for model pretraining on large
quantities of data have opened the way for similar foundation models in computer vision …

Convnext v2: Co-designing and scaling convnets with masked autoencoders

S Woo, S Debnath, R Hu, X Chen… - Proceedings of the …, 2023 - openaccess.thecvf.com
Driven by improved architectures and better representation learning frameworks, the field of
visual recognition has enjoyed rapid modernization and performance boost in the early …

Videomae v2: Scaling video masked autoencoders with dual masking

L Wang, B Huang, Z Zhao, Z Tong… - Proceedings of the …, 2023 - openaccess.thecvf.com
Scale is the primary factor for building a powerful foundation model that could well
generalize to a variety of downstream tasks. However, it is still challenging to train video …

Emergent correspondence from image diffusion

L Tang, M Jia, Q Wang, CP Phoo… - Advances in Neural …, 2023 - proceedings.neurips.cc
Finding correspondences between images is a fundamental problem in computer vision. In
this paper, we show that correspondence emerges in image diffusion models without any …

[HTML][HTML] Deep learning in food category recognition

Y Zhang, L Deng, H Zhu, W Wang, Z Ren, Q Zhou… - Information …, 2023 - Elsevier
Integrating artificial intelligence with food category recognition has been a field of interest for
research for the past few decades. It is potentially one of the next steps in revolutionizing …

SpectralGPT: Spectral remote sensing foundation model

D Hong, B Zhang, X Li, Y Li, C Li, J Yao… - … on Pattern Analysis …, 2024 - ieeexplore.ieee.org
The foundation model has recently garnered significant attention due to its potential to
revolutionize the field of visual representation learning in a self-supervised manner. While …

Self-supervised learning from images with a joint-embedding predictive architecture

M Assran, Q Duval, I Misra… - Proceedings of the …, 2023 - openaccess.thecvf.com
This paper demonstrates an approach for learning highly semantic image representations
without relying on hand-crafted data-augmentations. We introduce the Image-based Joint …

A metaverse: Taxonomy, components, applications, and open challenges

SM Park, YG Kim - IEEE access, 2022 - ieeexplore.ieee.org
Unlike previous studies on the Metaverse based on Second Life, the current Metaverse is
based on the social value of Generation Z that online and offline selves are not different …