12 citations · 23 across the 16 of their papers we have counts for
8 papers · 1 filter
360CityArena: A Realistic Virtual Urban Navigation Benchmark for Embodied Agents
Kenta Watanabe, Atsuyuki Miyai, Mizuki Takenawa +2
We present 360CityArena, a benchmark for evaluating the urban exploration capabilities of embodied agents within a photorealistic environment constructed from 360-degree videos. Ex…
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…
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…