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Showing papers from University of Tokyo Show all papers

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NeurIPS 2026U TokyoPekingNational Institute of InformaticThe University of Tokyo3D vision & reconstruction

Event based Multi-Velocity-Scale Imaging

Qiyao Gao, Peiqi Duan, Chu Zhou, Xinyu Zhou and 2 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026U TokyoRIKENThe University of Tokyo / RIKEN The University of Tokyo; RIKEN ARIKEN/University of TokyoSemi- & weakly supervised learning

Rethinking Learning from Label Proportions via Moment Matching

Tianhao Ma, Wei Wang, Yivan Zhang, Dong-Dong Wu and 3 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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76%Highly rated
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NeurIPS 2026U TokyoNagoyaMohamed bin Zayed University of MBZUAI / RIKEN AIPThe University of Tokyo / RIKEN LLM evaluation & benchmarks

CITE: Anytime Valid Statistical Inference in LLM Self-Consistency

CITE provides anytime-valid certification of a target answer as the unique mode under arbitrary data-dependent stopping without knowing the answer set, with optimal stopping-time rates and improved LLM self-consistency.

Hirofumi Ota, Naoto Iwase, Yuki Ichihara, Junpei Komiyama and 1 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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AI panel: 10 of 20 reviewers recommend it
lenient 4/5
medium 5/10
strict 1/5
80%Must read
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NeurIPS 2026U TokyoRikkyoRikkyo UniversityVision backbones

The Shape of Events: Edge-Based Inductive Biases via Cross-Domain Distillation

Cross-domain event-to-RGB distillation induces color invariant, shape-biased networks by suppressing texture dependence for edge-based robustness.

Shunsuke Yasuki, Masato Taki, Soshun Kihara

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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AI panel: 12 of 20 reviewers recommend it
lenient 5/5
medium 6/10
strict 1/5
91%Must read
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NeurIPS 2026KyotoThe University of TokyoHong Kong PolytechnicHohaiU Science and Technology of ChinVision-language models

JMed48k: A Multi-Profession Japanese Medical Licensing Benchmark for Vision-Language Model Evaluation

JMed48k introduces a Japanese medical licensing benchmark with 48,862 questions showing proprietary vision-language models gain substantially from images while medical-specific systems ignore visual evidence.

Yue Xun, Junyu Liu, Qian Niu, Xinyi Wang and 9 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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17/20 AI panelreviewers recommend it

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AI panel: 17 of 20 reviewers recommend it
lenient 5/5
medium 8/10
strict 4/5