15 citations · 20 across the 4 of their papers we have counts for
7 papers
Controllable Dynamic Multi-Task Architectures
Dripta S. Raychaudhuri, Yumin Suh, Samuel Schulter +4
Multi-task learning commonly encounters competition for resources among tasks, specifically when model capacity is limited. This challenge motivates models which allow control over…
On Generalizing Beyond Domains in Cross-Domain Continual Learning
Christian Simon, Masoud Faraki, Yi-Hsuan Tsai +5
Humans have the ability to accumulate knowledge of new tasks in varying conditions, but deep neural networks often suffer from catastrophic forgetting of previously learned knowled…
Learning Semantic Segmentation from Multiple Datasets with Label Shifts
Dongwan Kim, Yi-Hsuan Tsai, Yumin Suh +4
With increasing applications of semantic segmentation, numerous datasets have been proposed in the past few years. Yet labeling remains expensive, thus, it is desirable to jointly…
Cross-Domain Similarity Learning for Face Recognition in Unseen Domains
Masoud Faraki, Xiang Yu, Yi-Hsuan Tsai +2
Face recognition models trained under the assumption of identical training and test distributions often suffer from poor generalization when faced with unknown variations, such as…
Learning to Optimize Domain Specific Normalization for Domain Generalization
Seonguk Seo, Yumin Suh, Dongwan Kim +3
We propose a simple but effective multi-source domain generalization technique based on deep neural networks by incorporating optimized normalization layers that are specific to in…
Part-Aligned Bilinear Representations for Person Re-identification
Yumin Suh, Jingdong Wang, Siyu Tang +2
We propose a novel network that learns a part-aligned representation for person re-identification. It handles the body part misalignment problem, that is, body parts are misaligned…