Computer Vision · Generative AI · Multimodal Learning
Portfolio · Publications · Experience · Awards · CV
I explore how machines perceive, restore, and understand the visual world. My research connects generative models, robust visual learning, and multimodal understanding, with a focus on systems that work beyond clean, familiar conditions.
My research background spans National Taiwan University and the University of Washington, alongside industry research experience at Samsung Research, MediaTek, and ASML.
- Image Generation and Editing — diffusion priors, image restoration, and task-aware visual generation.
- Multimodal Learning — connecting visual understanding, generation, and retrieval.
- Domain Generalization — learning representations that remain useful in unfamiliar environments.
- Visual Understanding — semantic segmentation, crowd counting, and re-identification.
| Preview | Project |
|---|---|
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Restore, Assess, Repeat (RAR) CVPR 2026 · First author A unified framework that restores an image, assesses its quality, and repeats the process to handle unknown and composite degradations. Project · Paper · Video |
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RobustVisRAG CVPR 2026 · First author Causality-aware visual retrieval and grounded answer generation from documents affected by blur, noise, and other visual degradations. Project · Paper · Video |
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UniRestore CVPR 2025 Highlight · First author Bridging perceptual quality and downstream task needs through a unified image restoration model using a diffusion prior. Project · Paper · Video |
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PDAF ICCV 2025 · First author Probabilistic diffusion alignment and latent domain modeling for semantic segmentation beyond familiar conditions. Project · Paper · Video |
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APGCC ECCV 2024 · First author Auxiliary Point Guidance for more stable point-based crowd counting and localization. Project · Paper · Video |
Explore the full research collection and animated previews →
| Organization | Focus |
|---|---|
| University of Washington | Visiting research on diffusion models and image enhancement |
| National Taiwan University | Graduate research in image enhancement, re-identification, and crowd counting |
| Samsung Research UK | Unified multimodal understanding and generation |
| MediaTek | End-to-end learning-based video compression |
| ASML | Synthetic data generation for defect detection |
More about my research journey →
- NTU Outstanding Young Award — 2025
- CTCI Research Award — 2023
- Hon-Hai Tech Award — 2022
I'm happy to connect about research and collaboration in computer vision, generative AI, and multimodal learning.






