Search Results for author: Atsushi Hashimoto

Found 16 papers, 3 papers with code

Recipe Generation from Unsegmented Cooking Videos

no code implementations21 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.

Dense Video Captioning Recipe Generation

Edge-Selective Feature Weaving for Point Cloud Matching

no code implementations4 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.

WeaveNet for Approximating Assignment Problems

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.

Removing Word-Level Spurious Alignment between Images and Pseudo-Captions in Unsupervised Image Captioning

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.

Image Captioning image-sentence alignment

Divergence Optimization for Noisy Universal Domain Adaptation

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.

Universal Domain Adaptation

Visual Grounding Annotation of Recipe Flow Graph

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.

Visual Grounding

Partially-Shared Variational Auto-encoders for Unsupervised Domain Adaptation with Target Shift

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.

General Classification Pose Estimation +2

Procedural Text Generation from a Photo Sequence

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.

Text Generation

Decentralized Learning of Generative Adversarial Networks from Non-iid Data

no code implementations23 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.

Image Generation

Procedural Text Generation from an Execution Video

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.

Object Recognition Text Generation +1

Outlier Cluster Formation in Spectral Clustering

no code implementations3 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.

Face Clustering Outlier Detection +1

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