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Showing papers from Pohang University of Science and Technology Show all papers

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I2V-DETACH: Source Grounding Detachment for Unauthorized Image-to-Video Generation

Chanhui Lee, Yeonghwan Song, Yewon Kang, Jeany Son

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 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 2/5
medium 0/10
strict 0/5
45%Niche pick
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Transductive Generalization for GNNs via Optimal Transport

MoonJeong Park, Seungbeom Lee, Kyungmin Kim, Jaeseung Heo and 4 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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SeoulMMOD: A Large-Scale Multimodal Origin-Destination Flow Benchmark

Taeyoung Yu, Seonbin Jo, Jiwon Kim, Junyoung Byun

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

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1/20 AI panelreviewers recommend it

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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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Proactive Instance Navigation with Comparative Judgment for Ambiguous User Queries

Junhyuk Kwon, Seungjoon Lee, Hyejin Park, Kyle Min 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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AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
45%Niche pick
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Generalized Influence Functions for Better Model Change Estimates

Hyeonsu Lyu, Jonggyu Jang, Sehyun Ryu, Hyun Jong Yang

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 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
76%Highly rated
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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
strict 0/5
91%Must read
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MJ: Multi-turn LLM Jailbreaking via Decomposed Credit Assignment

DC-GRPO assigns turn-level group-relative credit in multi-turn LLM jailbreaking, achieving over 97% attack success and outperforming prior methods.

Junyoung Park, Namgyu Park, Sechan Lee, Yoon-Chan Jhi 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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17/20 AI panelreviewers recommend it

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AI panel: 17 of 20 reviewers recommend it
lenient 5/5
medium 10/10
strict 2/5
83%Must read
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Online Conformal Abstention for Factuality Control Under Adversarial Bandit Feedback

ExAUL provides online conformal abstention under adversarial bandit feedback with O(sqrt(T)) FDR control via feedback unlocking and a regret-to-FDR conversion lemma.

Minjae Lee, Yoonjae Jung, Sangdon Park

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

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13/20 AI panelreviewers recommend it

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AI panel: 13 of 20 reviewers recommend it
lenient 4/5
medium 6/10
strict 3/5
76%Highly rated
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Spectral-Aligned Pruning for Universal Error-Correcting Code Transformers

Spectral-Aligned Pruning uses code graph eigenvalues to retrieve reusable structured pruning masks for universal error-correcting transformers, recovering accuracy via LoRA adapters to cut computation and memory.

Sanghyeon Cho, Taewoo Park, Seong-Joon Park, Dae-Young Yun and 3 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: 10 of 20 reviewers recommend it
lenient 3/5
medium 7/10
strict 0/5
74%Highly rated
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Topology-Aware Representation Alignment for Semi-Supervised Vision-Language Learning

ToMA uses persistent homology to align cross-modal manifold edges via image-text pairs, improving semi-supervised vision-language learning in specialized domains.

Junwon You, Mihyun Jang, Sangwoo Mo, Jae-Hun Jung

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 3/5
medium 5/10
strict 1/5
88%Must read
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Hi-Q: Hierarchical Evidence-guided Query Refinement for Multi-Hop Question Answering

Hi-Q hierarchically refines multi-hop queries via evidence-guided resolution and expansion, outperforming iterative and graph-based retrieval baselines on full-corpus benchmarks.

Jueun Kim, Sungho Park, WOOK SHIN HAN

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026 · ▲ 35 on Hugging Face · Code ★ 1

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15/20 AI panelreviewers recommend it

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AI panel: 15 of 20 reviewers recommend it
lenient 4/5
medium 8/10
strict 3/5