325 citations · 1.7k across the 55 of their papers we have counts for
9 papers · 1 filter
Anomaly Clustering: Grouping Images into Coherent Clusters of Anomaly Types
Kihyuk Sohn, Jinsung Yoon, Chun-Liang Li +2
We study anomaly clustering, grouping data into coherent clusters of anomaly types. This is different from anomaly detection that aims to divide anomalies from normal data. Unlike…
Learning to Prompt for Continual Learning
Zifeng Wang, Zizhao Zhang, Chen-Yu Lee +7
The mainstream paradigm behind continual learning has been to adapt the model parameters to non-stationary data distributions, where catastrophic forgetting is the central challeng…
Learning Fast Sample Re-weighting Without Reward Data
Zizhao Zhang, Tomas Pfister
Training sample re-weighting is an effective approach for tackling data biases such as imbalanced and corrupted labels. Recent methods develop learning-based algorithms to learn sa…
ROPE: Reading Order Equivariant Positional Encoding for Graph-based Document Information Extraction
Chen-Yu Lee, Chun-Liang Li, Chu Wang +5
Natural reading orders of words are crucial for information extraction from form-like documents. Despite recent advances in Graph Convolutional Networks (GCNs) on modeling spatial…
Controlling Neural Networks with Rule Representations
Sungyong Seo, Sercan O. Arik, Jinsung Yoon +3
We propose a novel training method that integrates rules into deep learning, in a way the strengths of the rules are controllable at inference. Deep Neural Networks with Controllab…
Self-supervise, Refine, Repeat: Improving Unsupervised Anomaly Detection
Jinsung Yoon, Kihyuk Sohn, Chun-Liang Li +3
Anomaly detection (AD), separating anomalies from normal data, has many applications across domains, from security to healthcare. While most previous works were shown to be effecti…
Nested Hierarchical Transformer: Towards Accurate, Data-Efficient and Interpretable Visual Understanding
Zizhao Zhang, Han Zhang, Long Zhao +3
Hierarchical structures are popular in recent vision transformers, however, they require sophisticated designs and massive datasets to work well. In this paper, we explore the idea…
CutPaste: Self-Supervised Learning for Anomaly Detection and Localization
Chun-Liang Li, Kihyuk Sohn, Jinsung Yoon +1
We aim at constructing a high performance model for defect detection that detects unknown anomalous patterns of an image without anomalous data. To this end, we propose a two-stage…
Learning from Weakly-labeled Web Videos via Exploring Sub-Concepts
Kunpeng Li, Zizhao Zhang, Guanhang Wu +5
Learning visual knowledge from massive weakly-labeled web videos has attracted growing research interests thanks to the large corpus of easily accessible video data on the Internet…