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Showing papers from Hanyang Universty Show all papers

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Right Results, Wrong Reasons: Auditing Behavioral Reliance in Motion Forecasting

Geonyeong Park, Byounghun Park, Nayoung Kim, Kyungmin Kim and 1 more

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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2/20 AI panelreviewers recommend it

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AI panel: 2 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 1/5
57%Worth a look
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ReSMap: Recasting Satellite Priors for Robust and Accurate Online HD Map Construction

Kyungmin Kim, Sumin Lee, Sungoh Jeong, DoHyun Lim and 2 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
45%Niche pick
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SPERA: Spherical Prior EEG Foundation Model with Geometry- and Frequency-Aware Latent Prediction

Minsu Kim, Ye-Sung Kim, Hyeseong Jeon, Wooseok Hyung and 2 more

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

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AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
57%Worth a look
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SignRot: LLM Quantization with Massive Outlier-Aware Sign-Adjusted Rotation

Jaehun Gim, Jaewoo Kim, Gyuwan Kim, Seo Yeon Park

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

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AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
57%Worth a look
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Which Tokens to Merge? Diffusion Dynamics for Efficient Image Generation

SeungJu Cha, Ye-Chan Kim, HyunGee Kim, Sungho Koh and 1 more

Atlanta Poster Session 5, Fri, Dec 11, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
74%Highly rated
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ODDR: One-Step Deshadow Diffusion via Reward Guidance

ODDR achieves efficient, high-fidelity shadow removal without real-world paired supervision via one-step diffusion guided by a synthetic, annotation-free ShadowReward model, approaching fully supervised performance.

Junseong Shin, Kijun Kim, Minseong Kim, Dongjin Kim and 1 more

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

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9/20 AI panelreviewers recommend it

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AI panel: 9 of 20 reviewers recommend it
lenient 5/5
medium 4/10
strict 0/5