5 papers · 1 filter
Can VLM Pseudo-Labels Train a Time-Series QA Model That Outperforms the VLM?
Takuya Fujimura, Kota Dohi, Natsuo Yamashita +1
Time-series question answering (TSQA) tasks face significant challenges due to the lack of labeled data. Alternatively, with recent advancements in large-scale models, vision-langu…
Handling Domain Shifts for Anomalous Sound Detection: A Review of DCASE-Related Work
Kevin Wilkinghoff, Takuya Fujimura, Keisuke Imoto +3
When detecting anomalous sounds in complex environments, one of the main difficulties is that trained models must be sensitive to subtle differences in monitored target signals, wh…
ASDKit: A Toolkit for Comprehensive Evaluation of Anomalous Sound Detection Methods
Takuya Fujimura, Kevin Wilkinghoff, Keisuke Imoto +1
In this paper, we introduce ASDKit, a toolkit for anomalous sound detection (ASD) task. Our aim is to facilitate ASD research by providing an open-source framework that collects an…
Improving Anomalous Sound Detection through Pseudo-anomalous Set Selection and Pseudo-label Utilization under Unlabeled Conditions
Ibuki Kuroyanagi, Takuya Fujimura, Kazuya Takeda +1
This paper addresses performance degradation in anomalous sound detection (ASD) when neither sufficiently similar machine data nor operational state labels are available. We presen…
Analysis and Extension of Noisy-target Training for Unsupervised Target Signal Enhancement
Takuya Fujimura, Tomoki Toda
Deep neural network-based target signal enhancement (TSE) is usually trained in a supervised manner using clean target signals. However, collecting clean target signals is costly a…