Motoharu Sonogashira — accepted AI-conference papers

2025

FROSS: Faster-Than-Real-Time Online 3D Semantic Scene Graph Generation from RGB-D Images

Hao-Yu Hou, Chun-Yi Lee, Motoharu Sonogashira, Yasutomo Kawanishi

ICCV 2025poster

The ability to abstract complex 3D environments into simplified and structured representations is crucial across various domains. 3D semantic scene graphs (SSGs) achieve this by representing objects as nodes and their interrelationships as edges, facilitating high-level scene understanding. Existing…

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2020

Partially-Shared Variational Auto-encoders for Unsupervised Domain Adaptation with Target Shift

Ryuhei Takahashi, Atsushi Hashimoto, Motoharu Sonogashira, Masaaki Iiyama

ECCV 2020poster

This paper discusses unsupervised domain adaptation (UDA) with target shift, i.e., UDA with the non-identical label distributions of the source and target domains. In practice, this is an important problem; as we do not know labels in target domain datasets, we do not know whether or not its distrib…

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