activity
20172021
most citedSequential Gallery for Interactive Visual Design Optimization

88 citations · 88 across the 5 of their papers we have counts for

collaborators

6 papers

cs.LG2021

Tool- and Domain-Agnostic Parameterization of Style Transfer Effects Leveraging Pretrained Perceptual Metrics

Hiromu Yakura, Yuki Koyama, Masataka Goto

Current deep learning techniques for style transfer would not be optimal for design support since their "one-shot" transfer does not fit exploratory design processes. To overcome t…

cs.SD2020

Generative Melody Composition with Human-in-the-Loop Bayesian Optimization

Yijun Zhou, Yuki Koyama, Masataka Goto +1

Deep generative models allow even novice composers to generate various melodies by sampling latent vectors. However, finding the desired melody is challenging since the latent spac…

cs.CV2020

Lyric Video Analysis Using Text Detection and Tracking

Shota Sakaguchi, Jun Kato, Masataka Goto +1

We attempt to recognize and track lyric words in lyric videos. Lyric video is a music video showing the lyric words of a song. The main characteristic of lyric videos is that the l…

cs.GR202088 cited

Sequential Gallery for Interactive Visual Design Optimization

Yuki Koyama, Issei Sato, Masataka Goto

Visual design tasks often involve tuning many design parameters. For example, color grading of a photograph involves many parameters, some of which non-expert users might be unfami…

cs.CV2020

MirrorNet: A Deep Bayesian Approach to Reflective 2D Pose Estimation from Human Images

Takayuki Nakatsuka, Kazuyoshi Yoshii, Yuki Koyama +3

This paper proposes a statistical approach to 2D pose estimation from human images. The main problems with the standard supervised approach, which is based on a deep recognition (i…

cs.AI2017

Taste or Addiction?: Using Play Logs to Infer Song Selection Motivation

Kosetsu Tsukuda, Masataka Goto

Online music services are increasing in popularity. They enable us to analyze people's music listening behavior based on play logs. Although it is known that people listen to music…