2 citations · 4 across the 12 of their papers we have counts for
12 papers · 1 filter
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…
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…
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…
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…
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.,…
Unsupervised Part Discovery via Descriptor-Based Masked Image Restoration with Optimized Constraints
Jiahao Xia, Yike Wu, Wenjian Huang +2
Part-level features are crucial for image understanding, but few studies focus on them because of the lack of fine-grained labels. Although unsupervised part discovery can eliminat…
Cross-DINO: Cross the Deep MLP and Transformer for Small Object Detection
Guiping Cao, Wenjian Huang, Xiangyuan Lan +3
Small Object Detection (SOD) poses significant challenges due to limited information and the model's low class prediction score. While Transformer-based detectors have shown promis…
Open-Det: An Efficient Learning Framework for Open-Ended Detection
Guiping Cao, Tao Wang, Wenjian Huang +3
Open-Ended object Detection (OED) is a novel and challenging task that detects objects and generates their category names in a free-form manner, without requiring additional vocabu…
Mitigating Knowledge Discrepancies among Multiple Datasets for Task-agnostic Unified Face Alignment
Jiahao Xia, Min Xu, Wenjian Huang +3
Despite the similar structures of human faces, existing face alignment methods cannot learn unified knowledge from multiple datasets with different landmark annotations. The limite…
Continual Learning for Segment Anything Model Adaptation
Jinglong Yang, Yichen Wu, Jun Cen +3
Although the current different types of SAM adaptation methods have achieved promising performance for various downstream tasks, such as prompt-based ones and adapter-based ones, m…
Unsupervised Part Discovery via Dual Representation Alignment
Jiahao Xia, Wenjian Huang, Min Xu +4
Object parts serve as crucial intermediate representations in various downstream tasks, but part-level representation learning still has not received as much attention as other vis…
Sparse Local Patch Transformer for Robust Face Alignment and Landmarks Inherent Relation Learning
Jiahao Xia, Weiwei qu, Wenjian Huang +3
Heatmap regression methods have dominated face alignment area in recent years while they ignore the inherent relation between different landmarks. In this paper, we propose a Spars…