164 citations · 435 across the 12 of their papers we have counts for
24 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…
Visual Prompt Tuning for Generative Transfer Learning
Kihyuk Sohn, Yuan Hao, José Lezama +5
Transferring knowledge from an image synthesis model trained on a large dataset is a promising direction for learning generative image models from various domains efficiently. Whil…
Federated Semi-Supervised Learning with Prototypical Networks
Woojung Kim, Keondo Park, Kihyuk Sohn +2
With the increasing computing power of edge devices, Federated Learning (FL) emerges to enable model training without privacy concerns. The majority of existing studies assume the…
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…
Object-aware Contrastive Learning for Debiased Scene Representation
Sangwoo Mo, Hyunwoo Kang, Kihyuk Sohn +2
Contrastive self-supervised learning has shown impressive results in learning visual representations from unlabeled images by enforcing invariance against different data augmentati…