23 citations · 32 across the 8 of their papers we have counts for
9 papers
TextSR: Diffusion Super-Resolution with Multilingual OCR Guidance
Keren Ye, Ignacio Garcia Dorado, Michalis Raptis +4
While recent advancements in Image Super-Resolution (SR) using diffusion models have shown promise in improving overall image quality, their application to scene text images has re…
Unified Autoregressive Visual Generation and Understanding with Continuous Tokens
Lijie Fan, Luming Tang, Siyang Qin +11
We present UniFluid, a unified autoregressive framework for joint visual generation and understanding leveraging continuous visual tokens. Our unified autoregressive architecture p…
Hierarchical Text Spotter for Joint Text Spotting and Layout Analysis
Shangbang Long, Siyang Qin, Yasuhisa Fujii +2
We propose Hierarchical Text Spotter (HTS), a novel method for the joint task of word-level text spotting and geometric layout analysis. HTS can recognize text in an image and iden…
ICDAR 2023 Competition on Hierarchical Text Detection and Recognition
Shangbang Long, Siyang Qin, Dmitry Panteleev +3
We organize a competition on hierarchical text detection and recognition. The competition is aimed to promote research into deep learning models and systems that can jointly perfor…
Unified Line and Paragraph Detection by Graph Convolutional Networks
Shuang Liu, Renshen Wang, Michalis Raptis +1
We formulate the task of detecting lines and paragraphs in a document into a unified two-level clustering problem. Given a set of text detection boxes that roughly correspond to wo…
Towards End-to-End Unified Scene Text Detection and Layout Analysis
Shangbang Long, Siyang Qin, Dmitry Panteleev +3
Scene text detection and document layout analysis have long been treated as two separate tasks in different image domains. In this paper, we bring them together and introduce the t…
Self-supervision through Random Segments with Autoregressive Coding (RandSAC)
Tianyu Hua, Yonglong Tian, Sucheng Ren +3
Inspired by the success of self-supervised autoregressive representation learning in natural language (GPT and its variants), and advances in recent visual architecture design with…
Towards Unconstrained End-to-End Text Spotting
Siyang Qin, Alessandro Bissacco, Michalis Raptis +2
We propose an end-to-end trainable network that can simultaneously detect and recognize text of arbitrary shape, making substantial progress on the open problem of reading scene te…
Dependence Maximizing Temporal Alignment via Squared-Loss Mutual Information
Makoto Yamada, Leonid Sigal, Michalis Raptis +1
The goal of temporal alignment is to establish time correspondence between two sequences, which has many applications in a variety of areas such as speech processing, bioinformatic…