93 citations · 293 across the 17 of their papers we have counts for
27 papers
FormNetV2: Multimodal Graph Contrastive Learning for Form Document Information Extraction
Chen-Yu Lee, Chun-Liang Li, Hao Zhang +13
The recent advent of self-supervised pre-training techniques has led to a surge in the use of multimodal learning in form document understanding. However, existing approaches that…
SPADE: Semi-supervised Anomaly Detection under Distribution Mismatch
Jinsung Yoon, Kihyuk Sohn, Chun-Liang Li +2
Semi-supervised anomaly detection is a common problem, as often the datasets containing anomalies are partially labeled. We propose a canonical framework: Semi-supervised Pseudo-la…
Learning Instance-Specific Adaptation for Cross-Domain Segmentation
Yuliang Zou, Zizhao Zhang, Chun-Liang Li +3
We propose a test-time adaptation method for cross-domain image segmentation. Our method is simple: Given a new unseen instance at test time, we adapt a pre-trained model by conduc…
FormNet: Structural Encoding beyond Sequential Modeling in Form Document Information Extraction
Chen-Yu Lee, Chun-Liang Li, Timothy Dozat +7
Sequence modeling has demonstrated state-of-the-art performance on natural language and document understanding tasks. However, it is challenging to correctly serialize tokens in fo…
Decoupling Local and Global Representations of Time Series
Sana Tonekaboni, Chun-Liang Li, Sercan Arik +2
Real-world time series data are often generated from several sources of variation. Learning representations that capture the factors contributing to this variability enables a bett…
Towards Group Robustness in the presence of Partial Group Labels
Vishnu Suresh Lokhande, Kihyuk Sohn, Jinsung Yoon +3
Learning invariant representations is an important requirement when training machine learning models that are driven by spurious correlations in the datasets. These spurious correl…