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Showing papers from Institute for Computer Science, Artificial Intelligence and Technology Show all papers

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NeurIPS 2026KAUSTKing Abdullah University of ScieInstitute for Computer Science, U BristolCalifornia Academy of SciencesImage recognition

ReefNet: A Large-Scale Dataset and Benchmark for Fine-Grained Coral Reef Recognition

ReefNet provides ~925K genus-level coral annotations and benchmarks showing vision models degrade under zero-shot and cross-source shifts despite adaptation gains.

Abdulwahab Felemban, Yahia Battach, Faizan F Khan, Yuqian Fu and 12 more

Paris Poster Session 2, Wed, Dec 9, 5:00 PM–7:00 PM, Paris Poster Hall · 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 5/5
medium 8/10
strict 3/5
88%Must read
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NeurIPS 2026Beijing Academy of Artificial InShanghai JiaotongU Electronic Science and TechnolHainanThe Hong Kong University of Scie3D vision & reconstruction

Focusable Monocular Depth Estimation

FocusDepth uses spatially-aligned multi-scale prompt fusion to boost target-region depth accuracy and sharp boundaries while preserving global geometry, outperforming global baselines on FDE-Bench.

Yuxin Du, Tao Lin, Zile Zhong, Runting Li and 6 more

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

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15/20 AI panelreviewers recommend it

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AI panel: 15 of 20 reviewers recommend it
lenient 5/5
medium 8/10
strict 2/5
91%Must read
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NeurIPS 2026Mohamed bin Zayed University of MBZUAIThe University of TokyoInstitute for Computer Science, Probing & representation analysis

The Geometry of Forgetting: Temporal Knowledge Drift as an Independent Axis in LLM Representations

Temporal knowledge drift is geometrically orthogonal to correctness and uncertainty in LLM residual streams, making drift undetectable via standard signals despite linear probes reaching 0.83, 0.95 AUROC.

Rania Elbadry, Ahmed Heakl, Fan Zhang, Dani Bouch and 3 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 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 5/5
medium 9/10
strict 4/5
74%Highly rated
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NeurIPS 2026Shanghai JiaotongBeijing Academy of Artificial InTongjiShanghai JiaoTong UnversityInstitute for Computer Science, Vision-language-action models

Evo-Depth: A Lightweight Depth-Enhanced Vision-Language-Action Model

Evo-Depth is a lightweight 0.9-billion-parameter vision-language-action model using implicit depth encoding from RGB to improve spatial manipulation without extra sensors. It achieves top benchmark performance with minimal GPU memory and highest inference speed among compared methods.

Tao Lin, Yuxin Du, Jiting Liu, Nuobei Zhu and 13 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