Object detection is one of the most fundamental and challenging tasks to locate objects in
images and videos. Over the past, it has gained much attention to do more research on …

With the rise and development of deep learning, computer vision has been tremendously
transformed and reshaped. As an important research area in computer vision, scene text …

Large multimodal models trained on natural documents, which interleave images and text,
outperform models trained on image-text pairs on various multimodal benchmarks …

In this paper, we design and train a Generative Image-to-text Transformer, GIT, to unify
vision-language tasks such as image/video captioning and question answering. While …

Most visual recognition studies rely heavily on crowd-labelled data in deep neural networks
(DNNs) training, and they usually train a DNN for each single visual recognition task …

Context-aware STR methods typically use internal autoregressive (AR) language models
(LM). Inherent limitations of AR models motivated two-stage methods which employ an …

Linguistic knowledge is of great benefit to scene text recognition. However, how to effectively
model linguistic rules in end-to-end deep networks remains a research challenge. In this …

Multimodal large language models (MLLMs) building upon the foundation of powerful large
language models (LLMs) have recently demonstrated exceptional capabilities in generating …

Dominant scene text recognition models commonly contain two building blocks, a visual
model for feature extraction and a sequence model for text transcription. This hybrid …

In this paper, we abandon the dominant complex language model and rethink the linguistic
learning process in the scene text recognition. Different from previous methods considering …