Wenyang Liu

news

Jul 10, 2026
Our paper “DriftAD: Visually-Guided Text Drift for Few-Shot Industrial Anomaly Detection” has been accepted at ACM International Conference on Multimedia (ACM MM 2026). Joint work with Tianyi Liu, Dongshuo Zhang, Kejun Wu, and Adams Wai-Kin Kong.
Apr 29, 2026
One paper has been accepted at Information Fusion.
Dec 01, 2025
One paper has been accepted at Pattern Recognition.
Nov 30, 2025
Graduated with a Ph.D. in Electrical and Electronic Engineering from Nanyang Technological University.

research projects

Industrial Robotic Perception

Nov 2025 – Present Hyundai–NTU–A*STAR

Develop foundation-model-based anomaly detection, open-vocabulary perception and 6D pose estimation for inspecting and grasping unfamiliar industrial parts.

ACM MM

Human–Robot Collaboration

Jun 2025 – Nov 2025 Schaeffler–NTU

Evaluated RGB- and depth-based 3D human pose estimation and developed motion retargeting for human–robot collaboration.

Image Watermarking & Security

Dec 2024 – Jun 2025 Funded by DSO National Laboratories

Developed robust image watermarking and self-supervised watermark removal, evaluated under compression, noise and resizing.

JVCIR

Open-Vocabulary Industrial Vision

Mar 2024 – Dec 2024 Schaeffler–NTU

Developed open-vocabulary detection and segmentation, and studied human–object interaction recognition with limited supervision.

EMNLPACM MMKBS

Image and Video Restoration

Jan 2021 – Mar 2024 Funded by National Research Foundation

Developed image and video recovery methods for damaged flash storage, alongside lightweight image super-resolution models.

CVPRNeurIPSIEEE TMM

Efficient Video & AI Computing

Sep 2017 – Jul 2020 Chongqing University

Parallelized H.264 decoding for faster, lower-power video processing. Contributed to photonic accelerators for deep learning inference.

ICPADSDATE

selected publications

DriftAD: Visually-Guided Text Drift for Few-Shot Industrial Anomaly Detection

Wenyang Liu, Dongshuo Zhang, Tianyi Liu,

Proceedings of the ACM International Conference on Multimedia, 2026
ACM MM
CCF A

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We propose DriftAD, a visually-guided CLIP adaptation framework that adapts text representations to local visual context for few-shot industrial anomaly detection, achieving state-of-the-art results across 1-, 2-, and 4-shot settings with up to 1.5-point gains in AUROC and PRO.
@inproceedings{liu2026driftad, title = {{DriftAD}: Visually-Guided Text Drift for Few-Shot Industrial Anomaly Detection}, author = {Liu, Wenyang and Zhang, Dongshuo and Liu, Tianyi and Wu, Kejun and Kong, Wai Kin (Adams)}, booktitle = {Proceedings of the ACM International Conference on Multimedia}, year = {2026} }
PromptSR: Cascade Prompting for Lightweight Image Super-Resolution

Wenyang Liu, Chen Cai, Jianjun Gao,

IEEE Transactions on Multimedia, 2026
IEEE TMM
CCF A CAS Q1 Top JCR Q1

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We propose PromptSR, a cascade prompting framework for lightweight image super-resolution that leverages prompt-guided restoration to achieve efficient real-world image quality improvement.
@article{liu2026promptsr, title = {{PromptSR}: Cascade Prompting for Lightweight Image Super-Resolution}, author = {Liu, Wenyang and Cai, Chen and Gao, Jianjun and Wu, Kejun and Wang, Yi and Yap, Kim-Hui and Chau, Lap-Pui}, journal = {IEEE Transactions on Multimedia}, year = {2026}, publisher = {IEEE} }
SSH-Net: A Self-Supervised and Hybrid Network for Noisy Image Watermark Removal

Wenyang Liu, Jianjun Gao and Kim-Hui Yap

Journal of Visual Communication and Image Representation, 2025
JVCIR
CCF C CAS Q3 JCR Q2

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SSH-Net is a self-supervised hybrid network for removing noisy watermarks from images without requiring paired clean/watermarked training data.
@article{liu2025ssh, title = {{SSH-Net}: A Self-Supervised and Hybrid Network for Noisy Image Watermark Removal}, author = {Liu, Wenyang and Gao, Jianjun and Yap, Kim-Hui}, journal = {Journal of Visual Communication and Image Representation}, pages = {104516}, year = {2025}, publisher = {Academic Press} }
ByteNet: Rethinking Multimedia File Fragment Classification through Visual Perspectives

Wenyang Liu, Kejun Wu, Tianyi Liu,

IEEE Transactions on Multimedia, 2024
IEEE TMM
CCF A CAS Q1 Top JCR Q1

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We propose ByteNet, which rethinks file fragment classification by converting byte sequences into visual representations, enabling CNN-based analysis for robust digital forensics.
@article{liu2024bytenet, title = {{ByteNet}: Rethinking Multimedia File Fragment Classification through Visual Perspectives}, author = {Liu, Wenyang and Wu, Kejun and Liu, Tianyi and Wang, Yi and Yap, Kim-Hui and Chau, Lap-Pui}, journal = {IEEE Transactions on Multimedia}, volume = {27}, pages = {1305--1319}, year = {2024}, publisher = {IEEE} }
Bitstream-Corrupted JPEG Images Are Restorable: Two-Stage Compensation and Alignment Framework for Image Restoration

Wenyang Liu, Yi Wang, Kim-Hui Yap,

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023
CVPR
CCF A

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We propose a two-stage compensation and alignment framework to restore images from corrupted JPEG bitstreams, recovering damaged visual content through compensation and spatial alignment strategies.
@inproceedings{liu2023bitstream_cvpr, title = {Bitstream-Corrupted {JPEG} Images Are Restorable: Two-Stage Compensation and Alignment Framework for Image Restoration}, author = {Liu, Wenyang and Wang, Yi and Yap, Kim-Hui and Chau, Lap-Pui}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, pages = {9979--9988}, year = {2023} }
A Byte Sequence Is Worth an Image: CNN for File Fragment Classification Using Bit Shift and n-Gram Embeddings

Wenyang Liu, Yi Wang, Kejun Wu,

IEEE International Conference on Artificial Intelligence Circuits and Systems, 2023
AICAS Oral
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We demonstrate that byte sequences can be treated as images for file fragment classification, using bit shift and n-gram embeddings with CNN architectures.
@inproceedings{liu2023byte, title = {A Byte Sequence Is Worth an Image: {CNN} for File Fragment Classification Using Bit Shift and n-Gram Embeddings}, author = {Liu, Wenyang and Wang, Yi and Wu, Kejun and Yap, Kim-Hui and Chau, Lap-Pui}, booktitle = {IEEE International Conference on Artificial Intelligence Circuits and Systems}, year = {2023} }

MindReading: An Ultra-Low-Power Photonic Accelerator for EEG-Based Human Intention Recognition

Qian Lou^*, Wenyang Liu^*, Weichen Liu,

* Co-first authors

Asia and South Pacific Design Automation Conference, 2020

ASP-DAC

CCF C

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@inproceedings{lou2020mindreading,
  title = {{MindReading}: An Ultra-Low-Power Photonic Accelerator for {EEG}-Based Human Intention Recognition},
  author = {Lou, Qian and Liu, Wenyang and Liu, Weichen and Guo, Feng and Jiang, Lei},
  booktitle = {Asia and South Pacific Design Automation Conference},
  pages = {464--469},
  year = {2020},
  organization = {IEEE}
}

HolyLight: A Nanophotonic Accelerator for Deep Learning in Data Centers

Weichen Liu, Wenyang Liu, Yichen Ye,

Second author; first author was my master’s advisor

Design, Automation & Test in Europe Conference, 2019

DATE

CCF B 140+ citations

Abs Bib

@inproceedings{liu2019holylight,
  title = {{HolyLight}: A Nanophotonic Accelerator for Deep Learning in Data Centers},
  author = {Liu, Weichen and Liu, Wenyang and Ye, Yichen and Lou, Qian and Xie, Yiyuan and Jiang, Lei},
  booktitle = {Design, Automation \& Test in Europe Conference},
  pages = {1483--1488},
  year = {2019},
  organization = {IEEE}
}