32 citations · 47 across the 8 of their papers we have counts for
9 papers
DocTr: Document Transformer for Structured Information Extraction in Documents
Haofu Liao, Aruni RoyChowdhury, Weijian Li +6
We present a new formulation for structured information extraction (SIE) from visually rich documents. It aims to address the limitations of existing IOB tagging or graph-based for…
PolyFormer: Referring Image Segmentation as Sequential Polygon Generation
Jiang Liu, Hui Ding, Zhaowei Cai +4
In this work, instead of directly predicting the pixel-level segmentation masks, the problem of referring image segmentation is formulated as sequential polygon generation, and the…
MATrIX -- Modality-Aware Transformer for Information eXtraction
Thomas Delteil, Edouard Belval, Lei Chen +2
We present MATrIX - a Modality-Aware Transformer for Information eXtraction in the Visual Document Understanding (VDU) domain. VDU covers information extraction from visually rich…
Towards Differential Relational Privacy and its use in Question Answering
Simone Bombari, Alessandro Achille, Zijian Wang +6
Memorization of the relation between entities in a dataset can lead to privacy issues when using a trained model for question answering. We introduce Relational Memorization (RM) t…
Contrastive Neighborhood Alignment
Pengkai Zhu, Zhaowei Cai, Yuanjun Xiong +4
We present Contrastive Neighborhood Alignment (CNA), a manifold learning approach to maintain the topology of learned features whereby data points that are mapped to nearby represe…
Visual Relationship Detection Using Part-and-Sum Transformers with Composite Queries
Qi Dong, Zhuowen Tu, Haofu Liao +3
Computer vision applications such as visual relationship detection and human object interaction can be formulated as a composite (structured) set detection problem in which both th…
LayoutTransformer: Layout Generation and Completion with Self-attention
Kamal Gupta, Justin Lazarow, Alessandro Achille +3
We address the problem of scene layout generation for diverse domains such as images, mobile applications, documents, and 3D objects. Most complex scenes, natural or human-designed…
Toward Understanding Catastrophic Forgetting in Continual Learning
Cuong V. Nguyen, Alessandro Achille, Michael Lam +3
We study the relationship between catastrophic forgetting and properties of task sequences. In particular, given a sequence of tasks, we would like to understand which properties o…
Deep Convolutional Neural Networks for Eigenvalue Problems in Mechanics
David Finol, Yan Lu, Vijay Mahadevan +1
We show that deep convolutional neural networks (CNN) can massively outperform traditional densely-connected neural networks (both deep or shallow) in predicting eigenvalue problem…