29 citations · 123 across the 37 of their papers we have counts for
25 papers · 1 filter
Partition-and-Debias: Agnostic Biases Mitigation via A Mixture of Biases-Specific Experts
Jiaxuan Li, Duc Minh Vo, Hideki Nakayama
Bias mitigation in image classification has been widely researched, and existing methods have yielded notable results. However, most of these methods implicitly assume that a given…
Revisiting Latent Space of GAN Inversion for Real Image Editing
Kai Katsumata, Duc Minh Vo, Bei Liu +1
The exploration of the latent space in StyleGANs and GAN inversion exemplify impressive real-world image editing, yet the trade-off between reconstruction quality and editing quali…
Balancing Reconstruction and Editing Quality of GAN Inversion for Real Image Editing with StyleGAN Prior Latent Space
Kai Katsumata, Duc Minh Vo, Bei Liu +1
The exploration of the latent space in StyleGANs and GAN inversion exemplify impressive real-world image editing, yet the trade-off between reconstruction quality and editing quali…
A-CAP: Anticipation Captioning with Commonsense Knowledge
Duc Minh Vo, Quoc-An Luong, Akihiro Sugimoto +1
Humans possess the capacity to reason about the future based on a sparse collection of visual cues acquired over time. In order to emulate this ability, we introduce a novel task c…
Character-Centric Story Visualization via Visual Planning and Token Alignment
Hong Chen, Rujun Han, Te-Lin Wu +2
Story visualization advances the traditional text-to-image generation by enabling multiple image generation based on a complete story. This task requires machines to 1) understand…
Towards Parameter-Efficient Integration of Pre-Trained Language Models In Temporal Video Grounding
Erica K. Shimomoto, Edison Marrese-Taylor, Hiroya Takamura +3
This paper explores the task of Temporal Video Grounding (TVG) where, given an untrimmed video and a natural language sentence query, the goal is to recognize and determine tempora…
Pixel to Binary Embedding Towards Robustness for CNNs
Ikki Kishida, Hideki Nakayama
There are several problems with the robustness of Convolutional Neural Networks (CNNs). For example, the prediction of CNNs can be changed by adding a small magnitude of noise to a…
OSSGAN: Open-Set Semi-Supervised Image Generation
Kai Katsumata, Duc Minh Vo, Hideki Nakayama
We introduce a challenging training scheme of conditional GANs, called open-set semi-supervised image generation, where the training dataset consists of two parts: (i) labeled data…
NOC-REK: Novel Object Captioning with Retrieved Vocabulary from External Knowledge
Duc Minh Vo, Hong Chen, Akihiro Sugimoto +1
Novel object captioning aims at describing objects absent from training data, with the key ingredient being the provision of object vocabulary to the model. Although existing metho…
PPCD-GAN: Progressive Pruning and Class-Aware Distillation for Large-Scale Conditional GANs Compression
Duc Minh Vo, Akihiro Sugimoto, Hideki Nakayama
We push forward neural network compression research by exploiting a novel challenging task of large-scale conditional generative adversarial networks (GANs) compression. To this en…
Commonsense Knowledge Aware Concept Selection For Diverse and Informative Visual Storytelling
Hong Chen, Yifei Huang, Hiroya Takamura +1
Visual storytelling is a task of generating relevant and interesting stories for given image sequences. In this work we aim at increasing the diversity of the generated stories whi…
MADGAN: unsupervised Medical Anomaly Detection GAN using multiple adjacent brain MRI slice reconstruction
Changhee Han, Leonardo Rundo, Kohei Murao +7
Unsupervised learning can discover various unseen abnormalities, relying on large-scale unannotated medical images of healthy subjects. Towards this, unsupervised methods reconstru…
Meta Approach to Data Augmentation Optimization
Ryuichiro Hataya, Jan Zdenek, Kazuki Yoshizoe +1
Data augmentation policies drastically improve the performance of image recognition tasks, especially when the policies are optimized for the target data and tasks. In this paper,…
Bridging the gap between AI and Healthcare sides: towards developing clinically relevant AI-powered diagnosis systems
Changhee Han, Leonardo Rundo, Kohei Murao +2
Despite the success of Convolutional Neural Network-based Computer-Aided Diagnosis research, its clinical applications remain challenging. Accordingly, developing medical Artificia…
Empirical Study of Easy and Hard Examples in CNN Training
Ikki Kishida, Hideki Nakayama
Deep Neural Networks (DNNs) generalize well despite their massive size and capability of memorizing all examples. There is a hypothesis that DNNs start learning from simple pattern…
Faster AutoAugment: Learning Augmentation Strategies using Backpropagation
Ryuichiro Hataya, Jan Zdenek, Kazuki Yoshizoe +1
Data augmentation methods are indispensable heuristics to boost the performance of deep neural networks, especially in image recognition tasks. Recently, several studies have shown…
Synthesizing Diverse Lung Nodules Wherever Massively: 3D Multi-Conditional GAN-based CT Image Augmentation for Object Detection
Changhee Han, Yoshiro Kitamura, Akira Kudo +6
Accurate Computer-Assisted Diagnosis, relying on large-scale annotated pathological images, can alleviate the risk of overlooking the diagnosis. Unfortunately, in medical imaging,…
Learning More with Less: GAN-based Medical Image Augmentation
Changhee Han, Kohei Murao, Shin'ichi Satoh +1
Convolutional Neural Network (CNN)-based accurate prediction typically requires large-scale annotated training data. In Medical Imaging, however, both obtaining medical data and an…
USE-Net: incorporating Squeeze-and-Excitation blocks into U-Net for prostate zonal segmentation of multi-institutional MRI datasets
Leonardo Rundo, Changhee Han, Yudai Nagano +12
Prostate cancer is the most common malignant tumors in men but prostate Magnetic Resonance Imaging (MRI) analysis remains challenging. Besides whole prostate gland segmentation, th…
CNN-based Prostate Zonal Segmentation on T2-weighted MR Images: A Cross-dataset Study
Leonardo Rundo, Changhee Han, Jin Zhang +10
Prostate cancer is the most common cancer among US men. However, prostate imaging is still challenging despite the advances in multi-parametric Magnetic Resonance Imaging (MRI), wh…
Infinite Brain MR Images: PGGAN-based Data Augmentation for Tumor Detection
Changhee Han, Leonardo Rundo, Ryosuke Araki +4
Due to the lack of available annotated medical images, accurate computer-assisted diagnosis requires intensive Data Augmentation (DA) techniques, such as geometric/intensity transf…
Learning More with Less: Conditional PGGAN-based Data Augmentation for Brain Metastases Detection Using Highly-Rough Annotation on MR Images
Changhee Han, Kohei Murao, Tomoyuki Noguchi +5
Accurate Computer-Assisted Diagnosis, associated with proper data wrangling, can alleviate the risk of overlooking the diagnosis in a clinical environment. Towards this, as a Data…
Semantic Aware Attention Based Deep Object Co-segmentation
Hong Chen, Yifei Huang, Hideki Nakayama
Object co-segmentation is the task of segmenting the same objects from multiple images. In this paper, we propose the Attention Based Object Co-Segmentation for object co-segmentat…
Parameter Reference Loss for Unsupervised Domain Adaptation
Jiren Jin, Richard G. Calland, Takeru Miyato +2
The success of deep learning in computer vision is mainly attributed to an abundance of data. However, collecting large-scale data is not always possible, especially for the superv…