1 code implementation • 29 Jul 2022 • Saptarshi Sinha, Hiroki Ohashi, Katsuyuki Nakamura
Further, we use the difficulty measures of each class to design a novel weighted loss technique called `class-wise difficulty based weighted (CDB-W) loss' and a novel data sampling technique called `class-wise difficulty based sampling (CDB-S)'.
1 code implementation • 7 Sep 2021 • Katsuyuki Nakamura, Hiroki Ohashi, Mitsuhiro Okada
We compared the proposed sensor-fusion method with strong baselines on the MMAC Captions dataset and found that using sensor data as supplementary information to the egocentric-video data was beneficial, and that our proposed method outperformed the strong baselines, demonstrating the effectiveness of the proposed method.
1 code implementation • 5 Oct 2020 • Saptarshi Sinha, Hiroki Ohashi, Katsuyuki Nakamura
We claim that the 'difficulty' of a class as perceived by the model is more important to determine the weighting.
Ranked #1 on Long-tail Learning on EGTEA
no code implementations • 2 Aug 2018 • Hiroki Ohashi, Mohammad Al-Naser, Sheraz Ahmed, Katsuyuki Nakamura, Takuto Sato, Andreas Dengel
ZSL classifies instances of unseen classes, from which no training data is available, by utilizing the attributes of the classes.
no code implementations • CVPR 2017 • Katsuyuki Nakamura, Serena Yeung, Alexandre Alahi, Li Fei-Fei
Physiological signals such as heart rate can provide valuable information about an individual's state and activity.