We propose a novel multimodal architecture for Scene Text Visual Question Answering
(STVQA), named Layout-Aware Transformer (LaTr). The task of STVQA requires models to …

This paper aims to re-assess scene text recognition (STR) from a data-oriented perspective.
We begin by revisiting the six commonly used benchmarks in STR and observe a trend of …

Knowledge graph embedding (KGE) aims at learning powerful representations to benefit
various artificial intelligence applications. Meanwhile, contrastive learning has been widely …

Existing text recognition methods usually need large-scale training data. Most of them rely
on synthetic training data due to the lack of annotated real images. However, there is a …

Contrastive learning (CL) is widely known to require many negative samples, 65536 in
MoCo for instance, for which the performance of a dictionary-free framework is often inferior …

Pursuing accurate and robust recognizers has been a long-lasting goal for scene text
recognition (STR) researchers. Recently, attention-based methods have demonstrated their …

Recent years, graph contrastive learning (GCL), which aims to learn representations from
unlabeled graphs, has made great progress. However, the existing GCL methods mostly …

Abstract We introduce Perceiving Stroke-Semantic Context (PerSec), a new approach to self-
supervised representation learning tailored for Scene Text Recognition (STR) task …

Text images contain both visual and linguistic information. However, existing pre-training
techniques for text recognition mainly focus on either visual representation learning or …

Personalized recommender systems play a crucial role in capturing users' evolving
preferences over time to provide accurate and effective recommendations on various online …