no code implementations • 21 Sep 2022 • Taichi Nishimura, Atsushi Hashimoto, Yoshitaka Ushiku, Hirotaka Kameko, Shinsuke Mori
Based on this, we hypothesize that we can obtain correct recipes by selecting oracle events from the output events of the DVC model and re-generating sentences for them.
no code implementations • COLING 2022 • Keisuke Shirai, Atsushi Hashimoto, Taichi Nishimura, Hirotaka Kameko, Shuhei Kurita, Yoshitaka Ushiku, Shinsuke Mori
We present a new multimodal dataset called Visual Recipe Flow, which enables us to learn each cooking action result in a recipe text.
no code implementations • 4 Feb 2022 • rintaro yanagi, Atsushi Hashimoto, Shusaku Sone, Naoya Chiba, Jiaxin Ma, Yoshitaka Ushiku
By using the extracted discriminative edge features, our network can accurately calculate the correspondence between points.
no code implementations • 29 Sep 2021 • Shusaku Sone, Atsushi Hashimoto, Jiaxin Ma, rintaro yanagi, Naoya Chiba, Yoshitaka Ushiku
Assignment, a task to match a limited number of elements, is a fundamental problem in informatics.
1 code implementation • 6 Jul 2021 • Takehiko Ohkawa, Takuma Yagi, Atsushi Hashimoto, Yoshitaka Ushiku, Yoichi Sato
We validated our method on domain adaptation of hand segmentation from real and simulation images.
no code implementations • NeurIPS 2021 • Shusaku Sone, Jiaxin Ma, Atsushi Hashimoto, Naoya Chiba, Yoshitaka Ushiku
Assignment, a task to match a limited number of elements, is a fundamental problem in informatics.
1 code implementation • EACL 2021 • Ukyo Honda, Yoshitaka Ushiku, Atsushi Hashimoto, Taro Watanabe, Yuji Matsumoto
Unsupervised image captioning is a challenging task that aims at generating captions without the supervision of image-sentence pairs, but only with images and sentences drawn from different sources and object labels detected from the images.
no code implementations • CVPR 2021 • Qing Yu, Atsushi Hashimoto, Yoshitaka Ushiku
Hence, we consider a new realistic setting called Noisy UniDA, in which classifiers are trained with noisy labeled data from the source domain and unlabeled data with an unknown class distribution from the target domain.
no code implementations • LREC 2020 • Taichi Nishimura, Suzushi Tomori, Hayato Hashimoto, Atsushi Hashimoto, Yoko Yamakata, Jun Harashima, Yoshitaka Ushiku, Shinsuke Mori
Visual grounding is provided as bounding boxes to image sequences of recipes, and each bounding box is linked to an element of the workflow.
1 code implementation • ECCV 2020 • Ryuhei Takahashi, Atsushi Hashimoto, Motoharu Sonogashira, Masaaki Iiyama
In practice, this is an important problem in UDA; as we do not know labels in target domain datasets, we do not know whether or not its distribution is identical to that in the source domain dataset.
no code implementations • WS 2019 • Taichi Nishimura, Atsushi Hashimoto, Shinsuke Mori
Multimedia procedural texts, such as instructions and manuals with pictures, support people to share how-to knowledge.
no code implementations • 23 May 2019 • Ryo Yonetani, Tomohiro Takahashi, Atsushi Hashimoto, Yoshitaka Ushiku
This work addresses a new problem that learns generative adversarial networks (GANs) from multiple data collections that are each i) owned separately by different clients and ii) drawn from a non-identical distribution that comprises different classes.
no code implementations • CVPR 2018 • Yuki Fujimura, Masaaki Iiyama, Atsushi Hashimoto, Michihiko Minoh
Images captured in participating media such as murky water, fog, or smoke are degraded by scattered light.
no code implementations • IJCNLP 2017 • Atsushi Ushiku, Hayato Hashimoto, Atsushi Hashimoto, Shinsuke Mori
In this paper, we focus on procedure execution videos, in which a human makes or repairs something and propose a method for generating procedural texts from them.
no code implementations • 3 Mar 2017 • Takuro Ina, Atsushi Hashimoto, Masaaki Iiyama, Hidekazu Kasahara, Mikihiko Mori, Michihiko Minoh
The highlights of this paper are the following two mathematical observations: first, spectral clustering's intrinsic property of an outlier cluster formation, and second, the singularity of an outlier cluster with a valid cluster number.