This monograph surveys vision-language pre-training (VLP) methods for multimodal intelligence that have been developed in the last few years. We group these approaches …
J Li, D Li, S Savarese, S Hoi - International conference on …, 2023 - proceedings.mlr.press
The cost of vision-and-language pre-training has become increasingly prohibitive due to end-to-end training of large-scale models. This paper proposes BLIP-2, a generic and …
C Schuhmann, R Beaumont, R Vencu… - Advances in …, 2022 - proceedings.neurips.cc
Groundbreaking language-vision architectures like CLIP and DALL-E proved the utility of training on large amounts of noisy image-text data, without relying on expensive accurate …
We launch EVA, a vision-centric foundation model to explore the limits of visual representation at scale using only publicly accessible data. EVA is a vanilla ViT pre-trained …
We present ImageBind, an approach to learn a joint embedding across six different modalities-images, text, audio, depth, thermal, and IMU data. We show that all combinations …
The scaling of Transformers has driven breakthrough capabilities for language models. At present, the largest large language models (LLMs) contain upwards of 100B parameters …
C Saharia, W Chan, S Saxena, L Li… - Advances in neural …, 2022 - proceedings.neurips.cc
We present Imagen, a text-to-image diffusion model with an unprecedented degree of photorealism and a deep level of language understanding. Imagen builds on the power of …
M Cherti, R Beaumont, R Wightman… - Proceedings of the …, 2023 - openaccess.thecvf.com
Scaling up neural networks has led to remarkable performance across a wide range of tasks. Moreover, performance often follows reliable scaling laws as a function of training set …