activity
20202025
most citedTSRFormer: Table Structure Recognition with Transformers

9 citations · 22 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV2025★ 2 cited

Thinking With Videos: Multimodal Tool-Augmented Reinforcement Learning for Long Video Reasoning

Haoji Zhang, Xin Gu, Jiawen Li +7

The video reasoning ability of multimodal large language models (MLLMs) is crucial for downstream tasks like video question answering and temporal grounding. While recent approache…

cs.CV2024

Dynamic Relation Transformer for Contextual Text Block Detection

Jiawei Wang, Shunchi Zhang, Kai Hu +4

Contextual Text Block Detection (CTBD) is the task of identifying coherent text blocks within the complexity of natural scenes. Previous methodologies have treated CTBD as either a…

cs.CV2023★ 1 cited

Robust Table Structure Recognition with Dynamic Queries Enhanced Detection Transformer

Jiawei Wang, Weihong Lin, Chixiang Ma +4

We present a new table structure recognition (TSR) approach, called TSRFormer, to robustly recognizing the structures of complex tables with geometrical distortions from various ta…

cs.CV2022★ 9 cited

TSRFormer: Table Structure Recognition with Transformers

Weihong Lin, Zheng Sun, Chixiang Ma +4

We present a new table structure recognition (TSR) approach, called TSRFormer, to robustly recognizing the structures of complex tables with geometrical distortions from various ta…

cs.CV2022★ 6 cited

Robust Table Detection and Structure Recognition from Heterogeneous Document Images

Chixiang Ma, Weihong Lin, Lei Sun +1

We introduce a new table detection and structure recognition approach named RobusTabNet to detect the boundaries of tables and reconstruct the cellular structure of each table from…

cs.CV2020★ 4 cited

ReLaText: Exploiting Visual Relationships for Arbitrary-Shaped Scene Text Detection with Graph Convolutional Networks

Chixiang Ma, Lei Sun, Zhuoyao Zhong +1

We introduce a new arbitrary-shaped text detection approach named ReLaText by formulating text detection as a visual relationship detection problem. To demonstrate the effectivenes…