1 code implementation • 13 Sep 2023 • Shinnosuke Matsuo, Xiaomeng Wu, Gantugs Atarsaikhan, Akisato Kimura, Kunio Kashino, Brian Kenji Iwana, Seiichi Uchida
Unlike other learnable models using DTW for warping, our model predicts all local correspondences between two time series and is trained based on metric learning, which enables it to learn the optimal data-dependent warping for the target task.
1 code implementation • 5 Apr 2023 • Xiaomeng Wu, Yongqing Sun, Akisato Kimura
Most recent methods of deep image enhancement can be generally classified into two types: decompose-and-enhance and illumination estimation-centric.
1 code implementation • 14 Sep 2022 • Xiaomeng Wu, Yongqing Sun, Akisato Kimura, Kunio Kashino
Despite recent advances in image enhancement, it remains difficult for existing approaches to adaptively improve the brightness and contrast for both low-light and normal-light images.
1 code implementation • 14 Sep 2022 • Xiaomeng Wu, Takahito Kawanishi, Kunio Kashino
Existing image enhancement methods fall short of expectations because with them it is difficult to improve global and local image contrast simultaneously.
1 code implementation • 28 Mar 2021 • Shinnosuke Matsuo, Xiaomeng Wu, Gantugs Atarsaikhan, Akisato Kimura, Kunio Kashino, Brian Kenji Iwana, Seiichi Uchida
This approach adapts a parameterized attention model to time warping for greater and more adaptive temporal invariance.
no code implementations • ICCV 2015 • Xiaomeng Wu, Kunio Kashino
Hough voting in a geometric transformation space allows us to realize spatial verification, but remains sensitive to feature detection errors because of the inflexible quantization of single feature correspondences.