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20182026
most citedNeural Star Domain as Primitive Representation

8 citations · 36 across the 20 of their papers we have counts for

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13 papers · 1 filter

cs.CV2026

Linear Fusion MultiDiffusion for Fast Training-Free Spherical Panorama Generation

Akio Hayakawa, Yusuke Mukuta, Tatsuya Harada

We propose LF-MultiDiffusion, a training-free panorama generation method that extends MultiDiffusion to support linear projections between target and reference image spaces. Our ke…

cs.CV2025

DEJIMA: A Novel Large-scale Japanese Dataset for Image Captioning and Visual Question Answering

Toshiki Katsube, Taiga Fukuhara, Kenichiro Ando +3

This work addresses the scarcity of high-quality, large-scale resources for Japanese Vision-and-Language (V&L) modeling. We present a scalable and reproducible pipeline that integr…

cs.CV2024

Style-NeRF2NeRF: 3D Style Transfer From Style-Aligned Multi-View Images

Haruo Fujiwara, Yusuke Mukuta, Tatsuya Harada

We propose a simple yet effective pipeline for stylizing a 3D scene, harnessing the power of 2D image diffusion models. Given a NeRF model reconstructed from a set of multi-view im…

cs.CV2023

Fully Spiking Denoising Diffusion Implicit Models

Ryo Watanabe, Yusuke Mukuta, Tatsuya Harada

Spiking neural networks (SNNs) have garnered considerable attention owing to their ability to run on neuromorphic devices with super-high speeds and remarkable energy efficiencies.…

cs.CV2023

Domain Adaptive Multiple Instance Learning for Instance-level Prediction of Pathological Images

Shusuke Takahama, Yusuke Kurose, Yusuke Mukuta +7

Pathological image analysis is an important process for detecting abnormalities such as cancer from cell images. However, since the image size is generally very large, the cost of…

cs.CV2023

Self-Supervised Learning for Group Equivariant Neural Networks

Yusuke Mukuta, Tatsuya Harada

This paper proposes a method to construct pretext tasks for self-supervised learning on group equivariant neural networks. Group equivariant neural networks are the models whose st…