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researcher

Satoshi Tsutsui

Indiana University

27 papers hereh-index 15946 citations105 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author15
  • middle author12

Across the 27 of 27 papers where every author was matched, so the position is known.

fields
  • cs.CV17
  • cond-mat.supr-con4
  • cond-mat.mtrl-sci1
  • cond-mat.str-el1
  • cs.IR1
  • cs.MM1
affiliations
  • Indiana University
Homepage
same name
  • Satoshi Tsutsui — 7 papers, h 3
  • Satoshi Tsutsui — 2 papers, h 2
  • Satoshi Tsutsui — 1 paper
  • Satoshi Tsutsui — 1 paper, h 1
  • Satoshi Tsutsui — 1 paper, h 1

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20042023
most citedSoftening of Cu-O bond stretching phonon in tetragonal HgBa2​CuO4+δ​

90 citations · 256 across the 27 of their papers we have counts for

collaborators
Showing 2017Show all

4 papers · 1 filter

cs.CV2017

Minimizing Supervision for Free-space Segmentation

Satoshi Tsutsui, Tommi Kerola, Shunta Saito +1

Identifying "free-space," or safely driveable regions in the scene ahead, is a fundamental task for autonomous navigation. While this task can be addressed using semantic segmentat…

cs.CV2017★ 5 cited

Distantly Supervised Road Segmentation

Satoshi Tsutsui, Tommi Kerola, Shunta Saito

We present an approach for road segmentation that only requires image-level annotations at training time. We leverage distant supervision, which allows us to train our model using…

cs.CV2017★ 17 cited

Using Artificial Tokens to Control Languages for Multilingual Image Caption Generation

Satoshi Tsutsui, David Crandall

Recent work in computer vision has yielded impressive results in automatically describing images with natural language. Most of these systems generate captions in a sin- gle langua…

cs.CV2017★ 3 cited

A Data Driven Approach for Compound Figure Separation Using Convolutional Neural Networks

Satoshi Tsutsui, David Crandall

A key problem in automatic analysis and understanding of scientific papers is to extract semantic information from non-textual paper components like figures, diagrams, tables, etc.…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.