activity
20222026
most citedSparse Local Patch Transformer for Robust Face Alignment and Landmarks Inherent Relation Learning

2 citations · 4 across the 12 of their papers we have counts for

collaborators

14 papers

cs.CV2026

Curvature-Guided Mixing for MLLM Adaptation

Jinglong Yang, Jiaxuan He, Wenjian Huang +2

Fine-tuning Multimodal Large Language Models (MLLMs) on specialized tasks often leads to catastrophic forgetting of their general capabilities. Existing model merging methods to co…

cs.CL2026

Efficient Financial Language Understanding via Distillation with Synthetic Data

Wen-Fong, Huang, Edwin Simpson

Large instruction-following models are powerful but costly to deploy, particularly in finance, where labelled data are limited by confidentiality and expert annotation cost. We pre…

cs.CV2025

Enhancing Knowledge Transfer in Hyperspectral Image Classification via Cross-scene Knowledge Integration

Lu Huo, Wenjian Huang, Jianguo Zhang +2

Knowledge transfer has strong potential to improve hyperspectral image (HSI) classification, yet two inherent challenges fundamentally restrict effective cross-domain transfer: spe…

cs.CV2025

TripleFDS: Triple Feature Disentanglement and Synthesis for Scene Text Editing

Yuchen Bao, Yiting Wang, Wenjian Huang +5

Scene Text Editing (STE) aims to naturally modify text in images while preserving visual consistency, the decisive factors of which can be divided into three parts, i.e., text styl…

cs.CV2025

The 1st Solution for CARE Liver Task Challenge 2025: Contrast-Aware Semi-Supervised Segmentation with Domain Generalization and Test-Time Adaptation

Jincan Lou, Jingkun Chen, Haoquan Li +5

Accurate liver segmentation from contrast-enhanced MRI is essential for diagnosis, treatment planning, and disease monitoring. However, it remains challenging due to limited annota…

cs.CV2025

DS-Det: Single-Query Paradigm and Attention Disentangled Learning for Flexible Object Detection

Guiping Cao, Xiangyuan Lan, Wenjian Huang +3

Popular transformer detectors have achieved promising performance through query-based learning using attention mechanisms. However, the roles of existing decoder query types (e.g.,…