Good Papers

Showing papers from Nanyang Technology University, Singapore Show all papers

67%Highly rated
?Highly ratedVote to see the score
NeurIPS 2026City University of Hong KongNanyang TechnologicalU Hong KongHarbin Institute of TechnologyCity University of Hong KongVision-language models

MedVIGOR: Visual Evidence Internalization for Observation-Driven Reasoning in Medical VLMs

Yuan Wu, Jiayu Qian, Sipeng Wu, Songpan Gao and 5 more

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

– ReadersNo votes yet
2/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 2 of 20 reviewers recommend it
lenient 2/5
medium 0/10
strict 0/5
67%Highly rated
?Highly ratedVote to see the score
NeurIPS 2026Nanyang TechnologicalInstitute of Computing TechnologBeihangU the Chinese Academy of ScienceFudanAdversarial robustness

Omni-Safety under Cross-Modality Conflict: Vulnerabilities, Dynamic Mechanisms and Efficient Alignment

Kun Wang, Zherui Li, Zhenhong Zhou, Jie Zhang and 7 more

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

– ReadersNo votes yet
2/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 2 of 20 reviewers recommend it
lenient 1/5
medium 1/10
strict 0/5
89%Must read
?Must readVote to see the score
NeurIPS 2026SpotlightAnhui PolytechnicSUN YAT-SEN UNIVERSITYNanyang TechnologicalNanyang Technology University, SVision-language models

Text as Partial Constraint: Core–Residual Alignment for Robust Vision–Language Learning

TPC treats captions as partial constraints, aligning vision-language representations to a consensus semantic core while penalizing dependence on unsaid residuals, yielding robust zero-shot recognition and improved LVLM grounding.

Chengzhen Yu, Canran Xiao, SiYuan Ma, Yang Liu

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

– ReadersNo votes yet
16/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 16 of 20 reviewers recommend it
lenient 5/5
medium 9/10
strict 2/5