325 citations · 636 across the 29 of their papers we have counts for
7 papers · 1 filter
MGDoc: Pre-training with Multi-granular Hierarchy for Document Image Understanding
Zilong Wang, Jiuxiang Gu, Chris Tensmeyer +5
Document images are a ubiquitous source of data where the text is organized in a complex hierarchical structure ranging from fine granularity (e.g., words), medium granularity (e.g…
Delving into Out-of-Distribution Detection with Vision-Language Representations
Yifei Ming, Ziyang Cai, Jiuxiang Gu +3
Recognizing out-of-distribution (OOD) samples is critical for machine learning systems deployed in the open world. The vast majority of OOD detection methods are driven by a single…
User-Entity Differential Privacy in Learning Natural Language Models
Phung Lai, NhatHai Phan, Tong Sun +4
In this paper, we introduce a novel concept of user-entity differential privacy (UeDP) to provide formal privacy protection simultaneously to both sensitive entities in textual dat…
High-Quality Entity Segmentation
Lu Qi, Jason Kuen, Weidong Guo +5
Dense image segmentation tasks e.g., semantic, panoptic) are useful for image editing, but existing methods can hardly generalize well in an in-the-wild setting where there are unr…
Improving the Reliability for Confidence Estimation
Haoxuan Qu, Yanchao Li, Lin Geng Foo +3
Confidence estimation, a task that aims to evaluate the trustworthiness of the model's prediction output during deployment, has received lots of research attention recently, due to…
Meta Spatio-Temporal Debiasing for Video Scene Graph Generation
Li Xu, Haoxuan Qu, Jason Kuen +2
Video scene graph generation (VidSGG) aims to parse the video content into scene graphs, which involves modeling the spatio-temporal contextual information in the video. However, d…
Unified Pretraining Framework for Document Understanding
Jiuxiang Gu, Jason Kuen, Vlad I. Morariu +5
Document intelligence automates the extraction of information from documents and supports many business applications. Recent self-supervised learning methods on large-scale unlabel…