CoCGAN: Contrastive Learning for Adversarial Category Text Generation

no code implementations • COLING 2022 • Xin Sheng, Linli Xu, Yinlong Xu, Changcun Bao, Huang Chen, Bo Ren

The discriminator of CoCGAN discriminates the authenticity of given samples and optimizes a contrastive learning objective to capture both more flexible data-to-class relations and data-to-data relations among training samples.

Contrastive Learning Text Generation

Attention Where It Matters: Rethinking Visual Document Understanding with Selective Region Concentration

no code implementations • ICCV 2023 • Haoyu Cao, Changcun Bao, Chaohu Liu, Huang Chen, Kun Yin, Hao liu, Yinsong Liu, Deqiang Jiang, Xing Sun

We propose a novel end-to-end document understanding model called SeRum (SElective Region Understanding Model) for extracting meaningful information from document images, including document analysis, retrieval, and office automation.

document understanding Retrieval +1

Locate Then Generate: Bridging Vision and Language with Bounding Box for Scene-Text VQA

no code implementations • 4 Apr 2023 • Yongxin Zhu, Zhen Liu, Yukang Liang, Xin Li, Hao liu, Changcun Bao, Linli Xu

Different to conventional STVQA models which take the linguistic semantics and visual semantics in scene text as two separate features, in this paper, we propose a paradigm of "Locate Then Generate" (LTG), which explicitly unifies this two semantics with the spatial bounding box as a bridge connecting them.

Answer Generation Language Modelling +3