5 papers
Pre-training Vision Transformers with Formula-driven Supervised Learning
Hirokatsu Kataoka, Sora Takashima, Ryo Hayamizu +6
In the present work, we show that the performance of formula-driven supervised learning (FDSL) can match or even exceed that of ImageNet-21k and can approach that of the JFT-300M d…
Formula-Supervised Sound Event Detection: Pre-Training Without Real Data
Yuto Shibata, Keitaro Tanaka, Yoshiaki Bando +3
In this paper, we propose a novel formula-driven supervised learning (FDSL) framework for pre-training an environmental sound analysis model by leveraging acoustic signals parametr…
Leveraging LLMs with Iterative Loop Structure for Enhanced Social Intelligence in Video Question Answering
Erika Mori, Yue Qiu, Hirokatsu Kataoka +1
Social intelligence, the ability to interpret emotions, intentions, and behaviors, is essential for effective communication and adaptive responses. As robots and AI systems become…
MoireDB: Formula-generated Interference-fringe Image Dataset
Yuto Matsuo, Ryo Hayamizu, Hirokatsu Kataoka +1
Image recognition models have struggled to treat recognition robustness to real-world degradations. In this context, data augmentation methods like PixMix improve robustness but re…
Human Action Recognition without Human
Hirokatsu Kataoka, Kensho Hara, Yutaka Satoh
The objective of this paper is to evaluate "human action recognition without human". Motion representation is frequently discussed in human action recognition. We have examined sev…