1 code implementation • 25 Oct 2023 • Shangbang Long, Siyang Qin, Yasuhisa Fujii, Alessandro Bissacco, Michalis Raptis
We propose Hierarchical Text Spotter (HTS), a novel method for the joint task of word-level text spotting and geometric layout analysis.
1 code implementation • 16 May 2023 • Shangbang Long, Siyang Qin, Dmitry Panteleev, Alessandro Bissacco, Yasuhisa Fujii, Michalis Raptis
We organize a competition on hierarchical text detection and recognition.
2 code implementations • CVPR 2022 • Shangbang Long, Siyang Qin, Dmitry Panteleev, Alessandro Bissacco, Yasuhisa Fujii, Michalis Raptis
In this paper, we bring them together and introduce the task of unified scene text detection and layout analysis.
no code implementations • 22 Mar 2022 • Tianyu Hua, Yonglong Tian, Sucheng Ren, Michalis Raptis, Hang Zhao, Leonid Sigal
We illustrate that randomized serialization of the segments significantly improves the performance and results in distribution over spatially-long (across-segments) and -short (within-segment) predictions which are effective for feature learning.
no code implementations • 17 Mar 2022 • Shuang Liu, Renshen Wang, Michalis Raptis, Yasuhisa Fujii
We formulate the task of detecting lines and paragraphs in a document into a unified two-level clustering problem.
no code implementations • ICCV 2019 • Siyang Qin, Alessandro Bissacco, Michalis Raptis, Yasuhisa Fujii, Ying Xiao
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 text of irregular shape.
Instance Segmentation Optical Character Recognition (OCR) +3
no code implementations • NeurIPS 2013 • Nataliya Shapovalova, Michalis Raptis, Leonid Sigal, Greg Mori
We propose a new weakly-supervised structured learning approach for recognition and spatio-temporal localization of actions in video.
no code implementations • CVPR 2013 • Michalis Raptis, Leonid Sigal
We show classification performance that is competitive with the state of the art on the benchmark UT-Interaction dataset and illustrate that our model outperforms prior methods in an on-line streaming setting.
Ranked #3 on Human Interaction Recognition on UT
no code implementations • NeurIPS 2010 • Alper Ayvaci, Michalis Raptis, Stefano Soatto
We tackle the problem of simultaneously detecting occlusions and estimating optical flow.