12 citations · 27 across the 8 of their papers we have counts for
8 papers
Open-Set Domain Adaptation with Visual-Language Foundation Models
Qing Yu, Go Irie, Kiyoharu Aizawa
Unsupervised domain adaptation (UDA) has proven to be very effective in transferring knowledge obtained from a source domain with labeled data to a target domain with unlabeled dat…
LoCoOp: Few-Shot Out-of-Distribution Detection via Prompt Learning
Atsuyuki Miyai, Qing Yu, Go Irie +1
We present a novel vision-language prompt learning approach for few-shot out-of-distribution (OOD) detection. Few-shot OOD detection aims to detect OOD images from classes that are…
GL-MCM: Global and Local Maximum Concept Matching for Zero-Shot Out-of-Distribution Detection
Atsuyuki Miyai, Qing Yu, Go Irie +1
Zero-shot out-of-distribution (OOD) detection is a task that detects OOD images during inference with only in-distribution (ID) class names. Existing methods assume ID images conta…
Rethinking Rotation in Self-Supervised Contrastive Learning: Adaptive Positive or Negative Data Augmentation
Atsuyuki Miyai, Qing Yu, Daiki Ikami +2
Rotation is frequently listed as a candidate for data augmentation in contrastive learning but seldom provides satisfactory improvements. We argue that this is because the rotated…
Generalized Domain Adaptation
Yu Mitsuzumi, Go Irie, Daiki Ikami +1
Many variants of unsupervised domain adaptation (UDA) problems have been proposed and solved individually. Its side effect is that a method that works for one variant is often inef…
Multi-Task Curriculum Framework for Open-Set Semi-Supervised Learning
Qing Yu, Daiki Ikami, Go Irie +1
Semi-supervised learning (SSL) has been proposed to leverage unlabeled data for training powerful models when only limited labeled data is available. While existing SSL methods ass…