Seq-UPS: Sequential Uncertainty-aware Pseudo-label Selection for Semi-Supervised Text Recognition

31 August 2022

Papers citing "Seq-UPS: Sequential Uncertainty-aware Pseudo-label Selection for Semi-Supervised Text Recognition"

7 / 7 papers shown

Title
Entropy Heat-Mapping: Localizing GPT-Based OCR Errors with Sliding-Window Shannon Analysis Alexei Kaltchenko 56 0 0 30 Apr 2025
Improving Audio-Visual Video Parsing with Pseudo Visual Labels Jinxing Zhou Dan Guo Yiran Zhong Meng Wang VLM 39 13 0 04 Mar 2023
In all LikelihoodS: How to Reliably Select Pseudo-Labeled Data for Self-Training in Semi-Supervised Learning Julian Rodemann Christoph Jansen G. Schollmeyer Thomas Augustin 25 0 0 02 Mar 2023
In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning Mamshad Nayeem Rizve Kevin Duarte Yogesh S Rawat M. Shah 247 509 0 15 Jan 2021
Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results Antti Tarvainen Harri Valpola OOD MoMe 273 1,275 0 06 Mar 2017
COCO-Text: Dataset and Benchmark for Text Detection and Recognition in Natural Images Andreas Veit Tomas Matera Lukás Neumann Jirí Matas Serge J. Belongie 191 515 0 26 Jan 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 287 9,156 0 06 Jun 2015