activity
20172022
most citedFocusing Attention: Towards Accurate Text Recognition in Natural Images

584 citations · 595 across the 3 of their papers we have counts for

collaborators

8 papers

cs.CV20221 cited

Distilling Object Detectors With Global Knowledge

Sanli Tang, Zhongyu Zhang, Zhanzhan Cheng +4

Knowledge distillation learns a lightweight student model that mimics a cumbersome teacher. Existing methods regard the knowledge as the feature of each instance or their relations…

cs.CV202210 cited

Technical Report for ICCV 2021 Challenge SSLAD-Track3B: Transformers Are Better Continual Learners

Duo Li, Guimei Cao, Yunlu Xu +2

In the SSLAD-Track 3B challenge on continual learning, we propose the method of COntinual Learning with Transformer (COLT). We find that transformers suffer less from catastrophic…

cs.CV2021

Reciprocal Feature Learning via Explicit and Implicit Tasks in Scene Text Recognition

Hui Jiang, Yunlu Xu, Zhanzhan Cheng +5

Text recognition is a popular topic for its broad applications. In this work, we excavate the implicit task, character counting within the traditional text recognition, without add…

cs.CV2020

MANGO: A Mask Attention Guided One-Stage Scene Text Spotter

Liang Qiao, Ying Chen, Zhanzhan Cheng +4

Recently end-to-end scene text spotting has become a popular research topic due to its advantages of global optimization and high maintainability in real applications. Most methods…

cs.CV2020

Refined Gate: A Simple and Effective Gating Mechanism for Recurrent Units

Zhanzhan Cheng, Yunlu Xu, Mingjian Cheng +4

Recurrent neural network (RNN) has been widely studied in sequence learning tasks, while the mainstream models (e.g., LSTM and GRU) rely on the gating mechanism (in control of how…

cs.CV2019

Adversarial Seeded Sequence Growing for Weakly-Supervised Temporal Action Localization

Chengwei Zhang, Yunlu Xu, Zhanzhan Cheng +4

Temporal action localization is an important yet challenging research topic due to its various applications. Since the frame-level or segment-level annotations of untrimmed videos…