Joint image-text embedding is the bedrock for most Vision-and-Language (V+ L) tasks,
where multimodality inputs are simultaneously processed for joint visual and textual …

Joint image-text embedding is the bedrock for most Vision-and-Language (V+ L) tasks,
where multimodality inputs are jointly processed for visual and textual understanding. In this …

In this paper, we propose a single UniFied transfOrmer (UFO), which is capable of
processing either unimodal inputs (eg, image or language) or multimodal inputs (eg, the …

We propose Unicoder-VL, a universal encoder that aims to learn joint representations of
vision and language in a pre-training manner. Borrow ideas from cross-lingual pre-trained …

Vision-and-language pre-training has achieved impressive success in learning multimodal
representations between vision and language. To generalize this success to non-English …

In this paper, we introduce a new vision-language pre-trained model--ImageBERT--for
image-text joint embedding. Our model is a Transformer-based model, which takes different …

We propose Pixel-BERT to align image pixels with text by deep multi-modal transformers
that jointly learn visual and language embedding in a unified end-to-end framework. We aim …

Vision-and-language pretraining (VLP) aims to learn generic multimodal representations
from massive image-text pairs. While various successful attempts have been proposed …

Vision-language representation learning largely benefits from image-text alignment through
contrastive losses (eg, InfoNCE loss). The success of this alignment strategy is attributed to …

Abstract Vision-language (VL) pre-training has recently received considerable attention.
However, most existing end-to-end pre-training approaches either only aim to tackle VL …