12 citations · 41 across the 14 of their papers we have counts for
14 papers
A Benchmark and Evaluation for Real-World Out-of-Distribution Detection Using Vision-Language Models
Shiho Noda, Atsuyuki Miyai, Qing Yu +2
Out-of-distribution (OOD) detection is a task that detects OOD samples during inference to ensure the safety of deployed models. However, conventional benchmarks have reached perfo…
Generalized Out-of-Distribution Detection and Beyond in Vision Language Model Era: A Survey
Atsuyuki Miyai, Jingkang Yang, Jingyang Zhang +10
Detecting out-of-distribution (OOD) samples is crucial for ensuring the safety of machine learning systems and has shaped the field of OOD detection. Meanwhile, several other probl…
Unsolvable Problem Detection: Robust Understanding Evaluation for Large Multimodal Models
Atsuyuki Miyai, Jingkang Yang, Jingyang Zhang +7
This paper introduces a novel task to evaluate the robust understanding capability of Large Multimodal Models (LMMs), termed . Multiple…
Can Pre-trained Networks Detect Familiar Out-of-Distribution Data?
Atsuyuki Miyai, Qing Yu, Go Irie +1
Out-of-distribution (OOD) detection is critical for safety-sensitive machine learning applications and has been extensively studied, yielding a plethora of methods developed in the…
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…
Noisy Universal Domain Adaptation via Divergence Optimization for Visual Recognition
Qing Yu, Atsushi Hashimoto, Yoshitaka Ushiku
To transfer the knowledge learned from a labeled source domain to an unlabeled target domain, many studies have worked on universal domain adaptation (UniDA), where there is no con…
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…
Noisy Annotation Refinement for Object Detection
Jiafeng Mao, Qing Yu, Yoko Yamakata +1
Supervised training of object detectors requires well-annotated large-scale datasets, whose production is costly. Therefore, some efforts have been made to obtain annotations in ec…
Divergence Optimization for Noisy Universal Domain Adaptation
Qing Yu, Atsushi Hashimoto, Yoshitaka Ushiku
Universal domain adaptation (UniDA) has been proposed to transfer knowledge learned from a label-rich source domain to a label-scarce target domain without any constraints on the l…
The Aleatoric Uncertainty Estimation Using a Separate Formulation with Virtual Residuals
Takumi Kawashima, Qing Yu, Akari Asai +2
We propose a new optimization framework for aleatoric uncertainty estimation in regression problems. Existing methods can quantify the error in the target estimation, but they tend…
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…
Unsupervised Out-of-Distribution Detection by Maximum Classifier Discrepancy
Qing Yu, Kiyoharu Aizawa
Since deep learning models have been implemented in many commercial applications, it is important to detect out-of-distribution (OOD) inputs correctly to maintain the performance o…