Deep learning (DL) has emerged as a powerful subset of machine learning (ML) and
artificial intelligence (AI), outperforming traditional ML methods, especially in handling …

The field of deep learning has witnessed significant progress, particularly in computer vision
(CV), natural language processing (NLP), and speech. The use of large-scale models …

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 …

We propose a simple pairwise sigmoid loss for image-text pre-training. Unlike standard
contrastive learning with softmax normalization, the sigmoid loss operates solely on image …

Large language models (LLMs) have demonstrated tremendous capabilities in solving
complex tasks, from quantitative reasoning to understanding natural language. However …

The popularity of LLaMA (Touvron et al., 2023a; b) and other recently emerged moderate-
sized large language models (LLMs) highlights the potential of building smaller yet powerful …

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 …

Large Vision-Language Models (LVLMs) have recently played a dominant role in
multimodal vision-language learning. Despite the great success, it lacks a holistic evaluation …

Interpreting electron cryo-microscopy (cryo-EM) maps with atomic models requires high
levels of expertise and labour-intensive manual intervention in three-dimensional computer …

Large Language Models (LLMs) have demonstrated remarkable capabilities in important
tasks such as natural language understanding and language generation, and thus have the …