Search Results for author: Toshihiko Yamasaki

Found 34 papers, 14 papers with code

Learning from Synthetic Shadows for Shadow Detection and Removal

1 code implementation5 Jan 2021 Naoto Inoue, Toshihiko Yamasaki

To overcome this challenge, we present SynShadow, a novel large-scale synthetic shadow/shadow-free/matte image triplets dataset and a pipeline to synthesize it.

Shadow Detection And Removal Shadow Removal

Predicting Online Video Advertising Effects with Multimodal Deep Learning

no code implementations22 Dec 2020 Jun Ikeda, Hiroyuki Seshime, Xueting Wang, Toshihiko Yamasaki

With expansion of the video advertising market, research to predict the effects of video advertising is getting more attention.

Multimodal Deep Learning

Sparse Fooling Images: Fooling Machine Perception through Unrecognizable Images

no code implementations7 Dec 2020 Soichiro Kumano, Hiroshi Kera, Toshihiko Yamasaki

However, some images exist that lead DNNs to a completely wrong decision, whereas humans never fail with these images.

Out-of-Distribution Detection

Image inpainting using frequency domain priors

1 code implementation3 Dec 2020 Hiya Roy, Subhajit Chaudhury, Toshihiko Yamasaki, Tatsuaki Hashimoto

To alleviate these problems, we investigate if it is possible to obtain better performance by training the networks using frequency domain information (Discrete Fourier Transform) along with the spatial domain information.

Image Inpainting

Re-identification = Retrieval + Verification: Back to Essence and Forward with a New Metric

1 code implementation23 Nov 2020 Zheng Wang, Xin Yuan, Toshihiko Yamasaki, Yutian Lin, Xin Xu, Wenjun Zeng

In essence, current re-ID overemphasizes the importance of retrieval but underemphasizes that of verification, \textit{i. e.}, all returned images are considered as the target.

Image Retrieval

Pretext-Contrastive Learning: Toward Good Practices in Self-supervised Video Representation Leaning

1 code implementation29 Oct 2020 Li Tao, Xueting Wang, Toshihiko Yamasaki

It is convenient to treat PCL as a standard training strategy and apply it to many other works in self-supervised video feature learning.

Contrastive Learning Data Augmentation +4

Self-Play Reinforcement Learning for Fast Image Retargeting

no code implementations2 Oct 2020 Nobukatsu Kajiura, Satoshi Kosugi, Xueting Wang, Toshihiko Yamasaki

In this study, we address image retargeting, which is a task that adjusts input images to arbitrary sizes.

Self-supervised Video Representation Learning Using Inter-intra Contrastive Framework

2 code implementations6 Aug 2020 Li Tao, Xueting Wang, Toshihiko Yamasaki

With the proposed Inter-Intra Contrastive (IIC) framework, we can train spatio-temporal convolutional networks to learn video representations.

Action Recognition In Videos Contrastive Learning +5

Image Aesthetics Prediction Using Multiple Patches Preserving the Original Aspect Ratio of Contents

no code implementations5 Jul 2020 Lijie Wang, Xueting Wang, Toshihiko Yamasaki

The spread of social networking services has created an increasing demand for selecting, editing, and generating impressive images.

Motion Representation Using Residual Frames with 3D CNN

3 code implementations21 Jun 2020 Li Tao, Xueting Wang, Toshihiko Yamasaki

In this paper, we propose a fast but effective way to extract motion features from videos utilizing residual frames as the input data in 3D ConvNets.

Action Recognition Optical Flow Estimation

Investigating Generalization in Neural Networks under Optimally Evolved Training Perturbations

1 code implementation14 Mar 2020 Subhajit Chaudhury, Toshihiko Yamasaki

In this paper, we study the generalization properties of neural networks under input perturbations and show that minimal training data corruption by a few pixel modifications can cause drastic overfitting.

Domain Adaptation

Rethinking Motion Representation: Residual Frames with 3D ConvNets for Better Action Recognition

3 code implementations16 Jan 2020 Li Tao, Xueting Wang, Toshihiko Yamasaki

Further analysis indicates that better motion features can be extracted using residual frames with 3D ConvNets, and our residual-frame-input path is a good supplement for existing RGB-frame-input models.

Action Recognition Optical Flow Estimation

Assessing Robustness of Deep learning Methods in Dermatological Workflow

no code implementations15 Jan 2020 Sourav Mishra, Subhajit Chaudhury, Hideaki Imaizumi, Toshihiko Yamasaki

This paper aims to evaluate the suitability of current deep learning methods for clinical workflow especially by focusing on dermatology.

Weakly Supervised Video Summarization by Hierarchical Reinforcement Learning

no code implementations12 Jan 2020 Yiyan Chen, Li Tao, Xueting Wang, Toshihiko Yamasaki

For each subtask, the manager is trained to set a subgoal only by a task-level binary label, which requires much fewer labels than conventional approaches.

Hierarchical Reinforcement Learning Supervised Video Summarization

Unpaired Image Enhancement Featuring Reinforcement-Learning-Controlled Image Editing Software

no code implementations17 Dec 2019 Satoshi Kosugi, Toshihiko Yamasaki

This paper tackles unpaired image enhancement, a task of learning a mapping function which transforms input images into enhanced images in the absence of input-output image pairs.

Image Enhancement

Improving image classifiers for small datasets by learning rate adaptations

no code implementations26 Mar 2019 Sourav Mishra, Toshihiko Yamasaki, Hideaki Imaizumi

Our paper introduces an efficient combination of established techniques to improve classifier performance, in terms of accuracy and training time.

Fully Convolutional Network with Multi-Step Reinforcement Learning for Image Processing

1 code implementation10 Nov 2018 Ryosuke Furuta, Naoto Inoue, Toshihiko Yamasaki

This paper tackles a new problem setting: reinforcement learning with pixel-wise rewards (pixelRL) for image processing.

Image Denoising Image Restoration +1

Fast and Robust Estimation for Unit-Norm Constrained Linear Fitting Problems

no code implementations CVPR 2018 Daiki Ikami, Toshihiko Yamasaki, Kiyoharu Aizawa

M-estimator using iteratively reweighted least squares (IRLS) is one of the best-known methods for robust estimation.

Local and Global Optimization Techniques in Graph-Based Clustering

no code implementations CVPR 2018 Daiki Ikami, Toshihiko Yamasaki, Kiyoharu Aizawa

We propose a local optimization method, which is widely applicable to graph-based clustering cost functions.

Global Optimization

Object Detection for Comics using Manga109 Annotations

3 code implementations23 Mar 2018 Toru Ogawa, Atsushi Otsubo, Rei Narita, Yusuke Matsui, Toshihiko Yamasaki, Kiyoharu Aizawa

We annotated an existing image dataset of comics and created the largest annotation dataset, named Manga109-annotations.

Object Detection

Supervised classification of Dermatological diseases by Deep learning

no code implementations11 Feb 2018 Sourav Mishra, Toshihiko Yamasaki, Hideaki Imaizumi

This paper introduces a deep-learning based efficient classifier for common dermatological conditions, aimed at people without easy access to skin specialists.

Classification General Classification

PQTable: Non-exhaustive Fast Search for Product-quantized Codes using Hash Tables

no code implementations21 Apr 2017 Yusuke Matsui, Toshihiko Yamasaki, Kiyoharu Aizawa

In this paper, we propose a product quantization table (PQTable); a fast search method for product-quantized codes via hash-tables.

Quantization

Uncalibrated Photometric Stereo by Stepwise Optimization Using Principal Components of Isotropic BRDFs

no code implementations CVPR 2016 Keisuke Midorikawa, Toshihiko Yamasaki, Kiyoharu Aizawa

We propose a model that represents various isotropic reflectance functions by using the principal components of items in a dataset, and formulate the uncalibrated photometric stereo as a regression problem.

PQTable: Fast Exact Asymmetric Distance Neighbor Search for Product Quantization Using Hash Tables

no code implementations ICCV 2015 Yusuke Matsui, Toshihiko Yamasaki, Kiyoharu Aizawa

We propose the product quantization table (PQTable), a product quantization-based hash table that is fast and requires neither parameter tuning nor training steps.

Quantization

Sketch-based Manga Retrieval using Manga109 Dataset

no code implementations15 Oct 2015 Yusuke Matsui, Kota Ito, Yuji Aramaki, Toshihiko Yamasaki, Kiyoharu Aizawa

From the experiments, we verified that: (1) the retrieval accuracy of the proposed method is higher than those of previous methods; (2) the proposed method can localize an object instance with reasonable runtime and accuracy; and (3) sketch querying is useful for manga search.

Quantization

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