Yunlong Yu
特聘研究员
Works on machine learning and computer vision in ISEE's Department of Information and Communication Engineering.
Yunlong Yu's lab at Zhejiang works on signal processing & information theory. 32 papers since 2023, 10 at top venues, mostly TIP and Neurocomputing. Applications close .
At a glance
- papers, 2023–2026
- 8/ year
- at T1 venues
- 31%
- PhD students
- —
- MS students
- —
- alumni listed
- —
Papers by yearfrom DBLP, 2023–2026
- 4 papers2026 · T1 1 · collecting
- 9 papers2025 · T1 3
- 13 papers2024 · T1 4
- 6 papers2023 · T1 2
T1 venuesOtherVenues: TIP, Neurocomputing, Neural Networks, ECCV, CVPR
Research focuspaper titles that use each topic's words
- Signal processing & information theory0 papers
- isee0 papers
- information science0 papers
- information0 papers
- communication0 papers
Paper list
From DBLP.
20264 papers
- Knowl. Based Syst.Chat-MedGen: omni-adaptation of multi-modal large language models for diverse biomedical tasks (opens in a new tab)
- Neural NetworksImproving anomaly detection with foundation-model synthesis and wavelet-domain attention (opens in a new tab)
- IEEE Trans. Artif. Intell.MetaFusion: Instance-Aware Meta-Prompting and Token Refinement for VLMs Generalization (opens in a new tab)
- TPAMI (T1)Parameter-Efficient Fine-Tuning for Continual Learning: A Neural Tangent Kernel Perspective (opens in a new tab)
20259 papers
- TIP (T1)Balancing Feature Alignment and Uniformity for Few-Shot Classification (opens in a new tab)
- TIP (T1)Frequency-Spatial Complementation: Unified Channel-Specific Style Attack for Cross-Domain Few-Shot Learning (opens in a new tab)
- NeurocomputingHybrid mask generation for infrared small target detection with single-point supervision (opens in a new tab)
- NeurocomputingMask-free Iterative Refinement Network for weakly-supervised Few-shot Semantic Segmentation (opens in a new tab)
- Int. J. Multim. Inf. Retr.Multi-task classification network for few-shot learning (opens in a new tab)
- IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens.Multiscale Adapter Based on SAM for Remote Sensing Semantic Segmentation (opens in a new tab)
- TIP (T1)Relationship-Incremental Scene Graph Generation by a Divide-and-Conquer Pipeline With Feature Adapter (opens in a new tab)
- Neural NetworksSynth-CLIP: Synthetic data make CLIP generalize better in data-limited scenarios (opens in a new tab)
- IEEE Trans. Circuits Syst. Video Technol.Variational Adapter: Improving CLIP in Data-Imbalanced Scenarios (opens in a new tab)
202413 papers
- NeurocomputingA cognition-driven framework for few-shot class-incremental learning (opens in a new tab)
- PRCVCoMoFusion: Fast and High-Quality Fusion of Infrared and Visible Image with Consistency Model (opens in a new tab)
- NeurocomputingDense affinity matching for Few-Shot Segmentation (opens in a new tab)
- IEEE Geosci. Remote. Sens. Lett.Diversity-Infused Network for Unsupervised Few-Shot Remote Sensing Scene Classification (opens in a new tab)
- ECCV (T1)Improving Zero-Shot Generalization for CLIP with Variational Adapter (opens in a new tab)
- Knowl. Based Syst.Learning Multiple Criteria Calibration for Generalized Zero-shot Learning (opens in a new tab)
- TIP (T1)Model Attention Expansion for Few-Shot Class-Incremental Learning (opens in a new tab)
- ACM Trans. Multim. Comput. Commun. Appl.Multi-Content Interaction Network for Few-Shot Segmentation (opens in a new tab)
- TIP (T1)NTK-Guided Few-Shot Class Incremental Learning (opens in a new tab)
- WACVPixel Matching Network for Cross-Domain Few-Shot Segmentation (opens in a new tab)
- ECCV (T1)RCS-Prompt: Learning Prompt to Rearrange Class Space for Prompt-Based Continual Learning (opens in a new tab)
- IEEE Trans. Circuits Syst. Video Technol.Self-Prompting Perceptual Edge Learning for Dense Prediction (opens in a new tab)
20236 papers
- CAAI Trans. Intell. Technol.Deeply-Recursive Attention Network for video steganography (opens in a new tab)
- IJCAI (T1)DenseDINO: Boosting Dense Self-Supervised Learning with Token-Based Point-Level Consistency (opens in a new tab)
- CVPR (T1)GaitGCI: Generative Counterfactual Intervention for Gait Recognition (opens in a new tab)
- IEEE Trans. Neural Networks Learn. Syst.Knowledge Distillation Classifier Generation Network for Zero-Shot Learning (opens in a new tab)
- Pattern Recognit. Lett.Uncertainty-Aware Scene Graph Generation (opens in a new tab)
- Neural Comput. Appl.Zero-shot classification with unseen prototype learning (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.
Similar labsnearest by research summary
- CNTsinghua
Jinli Suo
- Computational imaging & photography
- CNSJTU
Songhua Liu
- Generative & foundation models
- USMIT
Phillip Isola
- Vision-language & multimodal
- USUCSD
Pamela C. Cosman
- Video understanding
- CNPeking
Yadong Mu
- Video understanding
- SGNTU
Deepu Rajan
- Video understanding
Your notes
Save this lab to keep notes.
Data
Some numbers could not be verified:
- The member list could not be found on the lab site.
- The alumni list could not be found on the lab site.
- Collected
- Verified
- Papers
- DBLP
- Members
- Not collected
- Alumni
- Not collected
Tiers are venue classifications, not lab rankings.
Zhejiangcloses