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Showing papers from Daegu Gyeongbuk Institute of Science and Technology Show all papers

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NeurIPS 2026DGISTDaegu Gyeongbuk Institute of SciDiffusion models

RB-LDC: Redundancy-Balanced Latent Coding for Robust Diffusion

Hyunseok Jeong, Jaeho Jeon, Young-Sik Kim

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

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NeurIPS 2026DGISTDaegu Gyeongbuk Institute of SciKorea Advanced Institute of ScieDeep RL

H-GenPO: Hierarchical Generative Policy Optimization via the Option-Critic Framework

Wonhyeok Choi, Minwoo Choi, Jaeyeul Kim, Kyumin Hwang and 2 more

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

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57%Worth a look
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NeurIPS 2026Daegu Gyeongbuk Institute of SciDGISTPose & motion

Learning Scene-Grounded Interaction Priors for Scene-Aware Human Motion Prediction

Donghyeon Soon, Yoon JI- Il, Hyeondong Kim, Daehee Park

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

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57%Worth a look
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NeurIPS 2026Korea Advanced Institute of ScieDaegu Gyeongbuk Institute of SciDGISTKorea Advanced Institute of ScieDynamic graphs

SRA: Spatial Reasoning Adapter via Evolving Social Interaction Graphs for Trajectory Prediction

Jaewoo Jeong, Seonkyu Song, Hyeonwoo Park, Jegyeong Cho and 4 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 2026GISTYonseiDaegu Gyeongbuk Institute of SciPohang University of Science andPose & motion

ComPose: When to Trust Hands for Object Pose Tracking

ComPose tracks 6DoF object pose in RGB video by using hand motions as complementary cues, achieving robust accuracy under severe occlusion without external priors.

Jisu Shin, Junoh Lee, JunGyu Lee, Inhwan Bae and 4 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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AI panel: 10 of 20 reviewers recommend it
lenient 5/5
medium 5/10
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80%Must read
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NeurIPS 2026U ArizonaDukeSung Kyun KwanPanasonic AI LabPanasonic Holdings Corp.Quantization

MoBiQuant: Mixture-of-Bits Quantization for Token-Adaptive Any-Precision LLM

MoBiQuant uses recursive residual quantization and token-aware routing to adapt weight precision per token, improving any-precision LLM inference speed and memory.

Dongwei Wang, Jinhee Kim, Seokho Han, Denis Gudovskiy and 7 more

Atlanta Poster Session 2, Wed, Dec 9, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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AI panel: 12 of 20 reviewers recommend it
lenient 5/5
medium 7/10
strict 0/5