Siliang Tang
教授
Works on natural language processing, multimodal large models, embodied intelligence and graph neural networks.
Siliang Tang's lab at Zhejiang works on multimodal & grounded language, large language models and vision-language & multimodal. 103 papers since 2023, 59 at top venues, mostly CVPR and ACM MM. Applications close .
At a glance
- papers, 2023–2026
- 27/ year
- at T1 venues
- 57%
- PhD students
- —
- MS students
- —
- alumni listed
- —
Papers by yearfrom DBLP, 2023–2026
- 19 papers2026 · T1 12 · collecting
- 38 papers2025 · T1 21
- 27 papers2024 · T1 19
- 19 papers2023 · T1 7
T1 venuesOtherVenues: CVPR, ACM MM, ICML, ICCV, NeurIPS
Research focuspaper titles that use each topic's words
- Large language models34 papers
- Multimodal & grounded language32 papers
- Vision-language & multimodal32 papers
- intelligence1 paper
- multimodal large0 papers
Paper list
From DBLP.
202619 papers
- Findings of ACLAHEAD: Attention Head Energy-Aware Dynamics for Hallucination Mitigation in MLLMs (opens in a new tab)
- CVPR (T1)Arcadia: Toward a Full-Lifecycle Framework for Embodied Lifelong Learning (opens in a new tab)
- Findings of ACLCoMoL: Efficient Mixture of LoRA Experts via Dynamic Core Space Merging (opens in a new tab)
- AAAI (T1)Evolving Generalist Virtual Agents with Generative and Associative Memory (opens in a new tab)
- ACM Trans. Inf. Syst.Graph Retrieval-Augmented Generation: A Survey (opens in a new tab)
- Knowl. Based Syst.IDEAL: Leveraging infinite and dynamic characterizations of large language models for query-focused summarization (opens in a new tab)
- Neural NetworksImproving large models with small models: Lower costs and better performance (opens in a new tab)
- CVPR (T1)Learning to Adapt: Self-Improving Web Agent via Cognitive-Aware Exploration (opens in a new tab)
- Expert Systems with ApplicationsMAKIMA: Tuning-free multi-attribute open-domain video editing via mask-guided attention modulation (opens in a new tab)
- ACL (T1)MoA: Heterogeneous Mixture of Adapters for Parameter-Efficient Fine-Tuning of Large Language Models (opens in a new tab)
- TPAMI (T1)Momentor++: Advancing Video Large Language Models With Fine-Grained Long Video Reasoning (opens in a new tab)
- ICICMSR-Net: Multi-model Synergistic Residual Network for Efficient Large-Small Model Collaboration (opens in a new tab)
202538 papers
- Findings of ACLAlign²LLaVA: Cascaded Human and Large Language Model Preference Alignment for Multi-modal Instruction Curation (opens in a new tab)
- CVPR (T1)AnyEdit: Mastering Unified High-Quality Image Editing for Any Idea (opens in a new tab)
- ICCV (T1)Benchmarking Multimodal CoT Reward Model Stepwise by Visual Program (opens in a new tab)
- ICML (T1)Boosting Virtual Agent Learning and Reasoning: A Step-Wise, Multi-Dimensional, and Generalist Reward Model with Benchmark (opens in a new tab)
- ACM MMChart-HQA: A Benchmark for Hypothetical Question Answering in Charts (opens in a new tab)
- Findings of ACLChatMap: Mining Human Thought Processes for Customer Service Chatbots via Multi-Agent Collaboration (opens in a new tab)
- AISTATS (T1)Choice is what matters after Attention (opens in a new tab)
- PRICAICombining Non-numerical Text and Numerical Sequences in LLM-Based Survival Prediction (opens in a new tab)
- NeurIPS (T1)Counterfactual Evolution of Multimodal Datasets via Visual Programming (opens in a new tab)
- IEEE Trans. Neural Networks Learn. Syst.DBA: Efficient Transformer With Dynamic Bilinear Low-Rank Attention (opens in a new tab)
- Frontiers Inf. Technol. Electron. Eng.E-CGL: an efficient continual graph learner (opens in a new tab)
- Artif. Intell. MedicineEvidenceMap: Learning evidence analysis to unleash the power of small language models for biomedical question answering (opens in a new tab)
202427 papers
- TPAMI (T1)Ask Questions With Double Hints: Visual Question Generation With Answer-Awareness and Region-Reference (opens in a new tab)
- ICML (T1)Auto-Encoding Morph-Tokens for Multimodal LLM (opens in a new tab)
- EMNLP (T1)Bridging Local Details and Global Context in Text-Attributed Graphs (opens in a new tab)
- NeurocomputingContrastive Hawkes graph neural networks with dynamic sampling for event prediction (opens in a new tab)
- AAAI (T1)Data Shunt: Collaboration of Small and Large Models for Lower Costs and Better Performance (opens in a new tab)
- ACM MMDe-fine: Decomposing and Refining Visual Programs with Auto-Feedback (opens in a new tab)
- ACM MMDEMON24: ACM MM24 Demonstrative Instruction Following Challenge (opens in a new tab)
- CVPR (T1)DIEM: Decomposition-Integration Enhancing Multimodal Insights (opens in a new tab)
- IJCAI (T1)Efficient Tuning and Inference for Large Language Models on Textual Graphs (opens in a new tab)
- ACM MMFact : Teaching MLLMs with Faithful, Concise and Transferable Rationales (opens in a new tab)
- MIPRFastLearn: A Rapid Learning Agent for Chat Models to Acquire Latest Knowledge (opens in a new tab)
- ICLR (T1)Fine-tuning Multimodal LLMs to Follow Zero-shot Demonstrative Instructions (opens in a new tab)
202319 papers
- Neural NetworksAttribute-driven streaming edge partitioning with reconciliations for distributed graph neural network training (opens in a new tab)
- ICML (T1)Continual Vision-Language Representation Learning with Off-Diagonal Information (opens in a new tab)
- IEEE Trans. Multim.Cross-Modal Data Augmentation for Tasks of Different Modalities (opens in a new tab)
- ACM MMDegeneration-Tuning: Using Scrambled Grid shield Unwanted Concepts from Stable Diffusion (opens in a new tab)
- ACM MMFedAA: Using Non-sensitive Modalities to Improve Federated Learning while Preserving Image Privacy (opens in a new tab)
- Findings of EMNLPGlobal Structure Knowledge-Guided Relation Extraction Method for Visually-Rich Document (opens in a new tab)
- ICCV (T1)Gradient-Regulated Meta-Prompt Learning for Generalizable Vision-Language Models (opens in a new tab)
- NeurocomputingGraph neural networks meet with distributed graph partitioners and reconciliations (opens in a new tab)
- AAAI (T1)KICE: A Knowledge Consolidation and Expansion Framework for Relation Extraction (opens in a new tab)
- ICCV (T1)Learning in Imperfect Environment: Multi-Label Classification with Long-Tailed Distribution and Partial Labels (opens in a new tab)
- ISWCNegative Sampling with Adaptive Denoising Mixup for Knowledge Graph Embedding (opens in a new tab)
- Findings of EMNLPReasoning Makes Good Annotators : An Automatic Task-specific Rules Distilling Framework for Low-resource Relation Extraction (opens in a new tab)
Where alumni went
Alumni are not listed on the lab homepage.
Apply to ZhejiangFall 2027
Computer Science PhD (international applicants)
- Round
- Self-funded
- Opens
- —
- GRE
- See program page
- English
- Per ZJU language requirements
- Fee
- RMB 800
Not yet confirmed for this intake.
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Data
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