28 citations · 47 across the 11 of their papers we have counts for
11 papers
Empowering Backbone Models for Visual Text Generation with Input Granularity Control and Glyph-Aware Training
Wenbo Li, Guohao Li, Zhibin Lan +5
Diffusion-based text-to-image models have demonstrated impressive achievements in diversity and aesthetics but struggle to generate images with legible visual texts. Existing backb…
FedTrans: Efficient Federated Learning via Multi-Model Transformation
Yuxuan Zhu, Jiachen Liu, Mosharaf Chowdhury +1
Federated learning (FL) aims to train machine learning (ML) models across potentially millions of edge client devices. Yet, training and customizing models for FL clients is notori…
FRSUM: Towards Faithful Abstractive Summarization via Enhancing Factual Robustness
Wenhao Wu, Wei Li, Jiachen Liu +4
Despite being able to generate fluent and grammatical text, current Seq2Seq summarization models still suffering from the unfaithful generation problem. In this paper, we study the…
Precisely the Point: Adversarial Augmentations for Faithful and Informative Text Generation
Wenhao Wu, Wei Li, Jiachen Liu +3
Though model robustness has been extensively studied in language understanding, the robustness of Seq2Seq generation remains understudied. In this paper, we conduct the first quant…
PLANET: Dynamic Content Planning in Autoregressive Transformers for Long-form Text Generation
Zhe Hu, Hou Pong Chan, Jiachen Liu +3
Despite recent progress of pre-trained language models on generating fluent text, existing methods still suffer from incoherence problems in long-form text generation tasks that re…
UNIMO-2: End-to-End Unified Vision-Language Grounded Learning
Wei Li, Can Gao, Guocheng Niu +5
Vision-Language Pre-training (VLP) has achieved impressive performance on various cross-modal downstream tasks. However, most existing methods can only learn from aligned image-cap…