With the urgent demand for generalized deep models, many pre-trained big models are
proposed, such as bidirectional encoder representations (BERT), vision transformer (ViT) …

In the past few years, the emergence of pre-training models has brought uni-modal fields
such as computer vision (CV) and natural language processing (NLP) to a new era …

Image-text retrieval is a fundamental and crucial branch in information retrieval. Although
much progress has been made in bridging vision and language, it remains challenging …

We present a new vision-language (VL) pre-training model dubbed Kaleido-BERT, which
introduces a novel kaleido strategy for fashion cross-modality representations from …

In the past few years, cross-modal image-text retrieval (ITR) has experienced increased
interest in the research community due to its excellent research value and broad real-world …

In this work, we construct the largest dataset for multimodal pretraining in Chinese, which
consists of over 1.9 TB images and 292GB texts that cover a wide range of domains. We …

Abstract recommendation, and marketing services. Extensive efforts have been made to
conquer the cross-modal retrieval problem in the general domain. When it comes to E …

In the fashion domain, there exists a variety of vision-and-language (V+ L) tasks, including
cross-modal retrieval, text-guided image retrieval, multi-modal classification, and image …

With the exponential surge in diverse multimodal data, traditional unimodal retrieval
methods struggle to meet the needs of users seeking access to data across various …

Pretrained models have produced great success in both Computer Vision (CV) and Natural
Language Processing (NLP). This progress leads to learning joint representations of vision …