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← Authors

Sheng-jun Huang

48 papers · 2010–2026 · 9 conferences · across top CS/AI conferences

Achievements

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+15 more ↓ 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird πŸŒ‰ Interdisciplinary Bridge πŸ—ΊοΈ Taxonomy Completionist (13) 🌍 Conference Polyglot (9)
🌈 Renaissance Researcher (7) πŸŒ‰ Interdisciplinary Bridge 🌍 Conference Polyglot (9) 🀝 Dynamic Duo (12) πŸ‘‘ Triple Crown πŸ† Keyword Champion (8) πŸ† Grand Slam πŸ”¬ Deep Specialist (15) πŸ“ˆ Trend Setter πŸš€ Conference Pioneer πŸ”₯ Unstoppable (11) ⚑ Prolific Year (5) ❓ The Questioner πŸ’Ž Century Club (46) πŸ—ƒοΈ Keyword Collector (193)

Conferences

AAAI (14) IJCAI (14) NIPS (8) ICML (4) CVPR (2) ECCV (2) ICCV (2) ACL (1) ICLR (1)

Top co-authors

Songcan Chen (12) Ming-Kun Xie (11) Chen-Chen Zong (6) Shao-Yuan Li (6) Ying-Peng Tang (5) Kun-Peng Ning (4) Ye-Wen Wang (4) Dong Liang (4) Lei Feng (4) Masashi Sugiyama (4)

Keywords

active learning (14) query strategy (5) multi-label classification (5) annotation cost (3) distribution shift (3) semi-supervised learning (3) multi-label learning (3) query selection (3) neural network optimization (3) neural collapse (2) label complexity (2) federated learning (2) feature representation (2) physics-informed neural network (2) sampling strategy (2) representation learning (2) uncertainty quantification (2) image restoration (2) adversarial training (2) label noise (2)

Papers

MultiMedBench: A Scenario-Aware Benchmark for Evaluating Knowledge Editing in Medical VQA AAAI 2026 Reflect Then Learn: Active Prompting for Information Extraction Guided by Introspective Confusion AAAI 2026 Inconsistency-Based Federated Active Learning IJCAI 2025 DM-POSA: Enhancing Open-World Test-Time Adaptation with Dual-Mode Matching and Prompt-Based Open Set Adaptation IJCAI 2025 FedDLAD: A Federated Learning Dual-Layer Anomaly Detection Framework for Enhancing Resilience Against Backdoor Attacks IJCAI 2025 Efficient Heterogeneity-Aware Federated Active Data Selection ICML 2025 Learning to Trust Bellman Updates: Selective State-Adaptive Regularization for Offline RL ICML 2025 Rethinking Epistemic and Aleatoric Uncertainty for Active Open-Set Annotation: An Energy-Based Approach CVPR 2025 Graph-guided Cross-composition Feature Disentanglement for Compositional Zero-shot Learning ACL 2025 Improving Generalization of Deep Neural Networks by Optimum Shifting AAAI 2025 MLC-NC: Long-Tailed Multi-Label Image Classification Through the Lens of Neural Collapse AAAI 2025 StructSR: Refuse Spurious Details in Real-World Image Super-Resolution AAAI 2025 Causality-enhanced Discreted Physics-informed Neural Networks for Predicting Evolutionary Equations IJCAI 2024 Optimistic Critic Reconstruction and Constrained Fine-Tuning for General Offline-to-Online RL NIPS 2024 Unlocking the Power of Open Set: A New Perspective for Open-Set Noisy Label Learning AAAI 2024 Dirichlet-Based Prediction Calibration for Learning with Noisy Labels AAAI 2024 Bidirectional Uncertainty-Based Active Learning for Open-Set Annotation ECCV 2024 Dual-Decoupling Learning and Metric-Adaptive Thresholding for Semi-Supervised Multi-Label Learning ECCV 2024 One-shot Active Learning Based on Lewis Weight Sampling for Multiple Deep Models ICLR 2024 Counterfactual Reasoning for Multi-Label Image Classification via Patching-Based Training ICML 2024 NanoAdapt: Mitigating Negative Transfer in Test Time Adaptation with Extremely Small Batch Sizes IJCAI 2024 Multi-Label Knowledge Distillation ICCV 2023 ALL-E: Aesthetics-guided Low-light Image Enhancement IJCAI 2023 Implicit Stochastic Gradient Descent for Training Physics-Informed Neural Networks AAAI 2023 Class-Distribution-Aware Pseudo-Labeling for Semi-Supervised Multi-Label Learning NIPS 2023 Improving Lens Flare Removal with General-Purpose Pipeline and Multiple Light Sources Recovery ICCV 2023 Active Learning for Open-Set Annotation CVPR 2022 Active Learning for Multiple Target Models NIPS 2022 Can Adversarial Training Be Manipulated By Non-Robust Features? NIPS 2022 Label-Aware Global Consistency for Multi-Label Learning with Single Positive Labels NIPS 2022 Improving Model Robustness by Adaptively Correcting Perturbation Levels with Active Queries AAAI 2021 Multi-Label Learning with Pairwise Relevance Ordering NIPS 2021 Better Safe Than Sorry: Preventing Delusive Adversaries with Adversarial Training NIPS 2021 Dual Active Learning for Both Model and Data Selection IJCAI 2021 Asynchronous Active Learning with Distributed Label Querying IJCAI 2021 Uncertainty Aware Graph Gaussian Process for Semi-Supervised Learning AAAI 2020 Cost-effectively Identifying Causal Effects When Only Response Variable is Observable ICML 2020 Active Learning with Query Generation for Cost-Effective Text Classification AAAI 2020 Partial Multi-Label Learning with Noisy Label Identification AAAI 2020 Self-Paced Active Learning: Query the Right Thing at the Right Time AAAI 2019 Active Sampling for Open-Set Classification without Initial Annotation AAAI 2019 Multi-View Active Learning for Video Recommendation IJCAI 2019 Cost-Effective Active Learning for Hierarchical Multi-Label Classification IJCAI 2018 Multi-instance multi-label active learning IJCAI 2017 Cost-Effective Active Learning from Diverse Labelers IJCAI 2017 Transfer Learning with Active Queries from Source Domain IJCAI 2016 Multi-Label Active Learning: Query Type Matters IJCAI 2015 Active Learning by Querying Informative and Representative Examples NIPS 2010