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Showing papers from College of Computer Science, Sichuan University Show all papers

67%Highly rated
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scMAF: Single-Cell Multi-Omics Clustering via Adaptive Modality Fusion

Jun Fu, Yiding Lu, Ruohong Yang, Xi Peng and 1 more

Sydney Poster Session 4, Wed, Dec 9, 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 2/5
medium 0/10
strict 0/5
71%Highly rated
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Learning Subspace-Preserving Sparse Attention Graphs from Heterogeneous Multiview Data

SAGL learns subspace-preserving sparse attention graphs from heterogeneous multiview data via bilinear attention and dynamic sparsity gating, outperforming state-of-the-art unsupervised transfer learning methods.

Jie Chen, Yuanbiao Gou, Chuanbin Liu, Zhu Wang and 1 more

Paris Poster Session 3, Thu, Dec 10, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026

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6/20 AI panelreviewers recommend it

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AI panel: 6 of 20 reviewers recommend it
lenient 2/5
medium 4/10
strict 0/5
91%Must read
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Attention Transfer Is Not Universally Effective for Vision Transformers

Attention transfer fails for four ViT families due to architectural mismatch, and adding the teacher's native components to students fully restores its effectiveness.

Huaiyuan Qin, Muli Yang, Gabriel James Goenawan, Peng Hu and 3 more

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

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18/20 AI panelreviewers recommend it

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AI panel: 18 of 20 reviewers recommend it
lenient 4/5
medium 10/10
strict 4/5
89%Must read
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ARK: A Dual-Axis Multimodal Retrieval Benchmark along Reasoning and Knowledge

ARK introduces a dual-axis multimodal retrieval benchmark spanning knowledge domains and reasoning skills, revealing persistent bottlenecks in fine-grained visual and spatial reasoning.

Yijie Lin, Guofeng Ding, Haochen Zhou, Haobin Li and 2 more

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

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16/20 AI panelreviewers recommend it

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AI panel: 16 of 20 reviewers recommend it
lenient 4/5
medium 9/10
strict 3/5
76%Highly rated
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Robust Multi-view Clustering against Imperfect Information

PLCI proposes a unified robust multi-view clustering framework that infers latent cross-view counterparts via posterior guidance to simultaneously handle incomplete views and noisy correspondences.

Zhichao Huang, Haochen Zhou, Hao Wang, Xi Peng and 1 more

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

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10/20 AI panelreviewers recommend it

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AI panel: 10 of 20 reviewers recommend it
lenient 4/5
medium 6/10
strict 0/5