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20232026
most citedCross-modulated Attention Transformer for RGBT Tracking

1 citations · 1 across the 3 of their papers we have counts for

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cs.CV2026

TDATR: Improving End-to-End Table Recognition via Table Detail-Aware Learning and Cell-Level Visual Alignment

Chunxia Qin, Chenyu Liu, Pengcheng Xia +4

Tables are pervasive in diverse documents, making table recognition (TR) a fundamental task in document analysis. Existing modular TR pipelines separately model table structure and…

cs.CV2025

Col-OLHTR: A Novel Framework for Multimodal Online Handwritten Text Recognition

Chenyu Liu, Jinshui Hu, Baocai Yin +4

Online Handwritten Text Recognition (OLHTR) has gained considerable attention for its diverse range of applications. Current approaches usually treat OLHTR as a sequence recognitio…

cs.CV20241 cited

Cross-modulated Attention Transformer for RGBT Tracking

Yun Xiao, Jiacong Zhao, Andong Lu +4

Existing Transformer-based RGBT trackers achieve remarkable performance benefits by leveraging self-attention to extract uni-modal features and cross-attention to enhance multi-mod…

cs.CV2023

1DFormer: a Transformer Architecture Learning 1D Landmark Representations for Facial Landmark Tracking

Shi Yin, Shijie Huan, Shangfei Wang +5

Recently, heatmap regression methods based on 1D landmark representations have shown prominent performance on locating facial landmarks. However, previous methods ignored to make d…

cs.CV2023

Weakly Supervised Scene Text Generation for Low-resource Languages

Yangchen Xie, Xinyuan Chen, Hongjian Zhan +4

A large number of annotated training images is crucial for training successful scene text recognition models. However, collecting sufficient datasets can be a labor-intensive and c…

cs.CV2023

VGTS: Visually Guided Text Spotting for Novel Categories in Historical Manuscripts

Wenbo Hu, Hongjian Zhan, Xinchen Ma +3

In the field of historical manuscript research, scholars frequently encounter novel symbols in ancient texts, investing considerable effort in their identification and documentatio…