3 citations · 5 across the 12 of their papers we have counts for
15 papers
EvolveNet: Collaborative Harness Evolution for Agent Self-Improvement
Jun Nie, Yonggang Zhang, Qianshu Cai +3
An LLM agent can acquire persistent improvements by evolving its harness---the executable program governing model calls, tool use, verification, and recovery---without updating mod…
Generated Images Are Easier to Forget: A Machine Unlearning Perspective for Synthetic Image Detection
Jun Nie, Yonggang Zhang, Tongliang Liu +3
Robust detection of generated images is critical to counter the misuse of generative models. Existing methods primarily depend on learning from human-annotated training datasets, l…
Hidden Human-Like Nature of Machine-Generated Texts: Theory and Detection Enhancement
Chenwang Wu, Yiu-ming Cheung, Bo Han +1
Machine-generated texts (MGTs) produced by large language models (LLMs) are increasingly prevalent across various applications, while their potential misuse in fake news propagatio…
Multi-Level Contextual Token Relation Modeling for Machine-Generated Text Detection
Chenwang Wu, Yiuming Cheung, Bo Han +2
Machine-generated texts (MGTs) pose risks such as disinformation and phishing, underscoring the need for reliable detection. Metric-based methods, which extract statistically disti…
Adding Thermal Awareness to Visual Systems in Real-Time via Distilled Diffusion Models
Yuchen Guo, Junli Gong, Wenjun Dong +2
Purely RGB-based vision models often fail to provide reliable cues in challenging scenarios such as nighttime and fog, leading to degraded performance and safety risks. Infrared im…
Beyond Raw Detection Scores: Markov-Informed Calibration for Boosting Machine-Generated Text Detection
Chenwang Wu, Yiu-ming Cheung, Shuhai Zhang +2
While machine-generated texts (MGTs) offer great convenience, they also pose risks such as disinformation and phishing, highlighting the need for reliable detection. Metric-based m…
Advancing Machine-Generated Text Detection from an Easy to Hard Supervision Perspective
Chenwang Wu, Yiu-ming Cheung, Bo Han +1
Existing machine-generated text (MGT) detection methods implicitly assume labels as the "golden standard". However, we reveal boundary ambiguity in MGT detection, implying that tra…
FedGPS: Statistical Rectification Against Data Heterogeneity in Federated Learning
Zhiqin Yang, Yonggang Zhang, Chenxin Li +3
Federated Learning (FL) confronts a significant challenge known as data heterogeneity, which impairs model performance and convergence. Existing methods have made notable progress…
Semantic-guided Fine-tuning of Foundation Model for Long-tailed Visual Recognition
Yufei Peng, Yonggang Zhang, Yiu-ming Cheung
The variance in class-wise sample sizes within long-tailed scenarios often results in degraded performance in less frequent classes. Fortunately, foundation models, pre-trained on…
Epistemic Uncertainty for Generated Image Detection
Jun Nie, Yonggang Zhang, Tongliang Liu +3
We introduce a novel framework for AI-generated image detection through epistemic uncertainty, aiming to address critical security concerns in the era of generative models. Our key…
GaussianMarker: Uncertainty-Aware Copyright Protection of 3D Gaussian Splatting
Xiufeng Huang, Ruiqi Li, Yiu-ming Cheung +3
3D Gaussian Splatting (3DGS) has become a crucial method for acquiring 3D assets. To protect the copyright of these assets, digital watermarking techniques can be applied to embed…
FuseFL: One-Shot Federated Learning through the Lens of Causality with Progressive Model Fusion
Zhenheng Tang, Yonggang Zhang, Peijie Dong +4
One-shot Federated Learning (OFL) significantly reduces communication costs in FL by aggregating trained models only once. However, the performance of advanced OFL methods is far b…
Interpreting and Improving Large Language Models in Arithmetic Calculation
Wei Zhang, Chaoqun Wan, Yonggang Zhang +4
Large language models (LLMs) have demonstrated remarkable potential across numerous applications and have shown an emergent ability to tackle complex reasoning tasks, such as mathe…
Ask, Attend, Attack: A Effective Decision-Based Black-Box Targeted Attack for Image-to-Text Models
Qingyuan Zeng, Zhenzhong Wang, Yiu-ming Cheung +1
While image-to-text models have demonstrated significant advancements in various vision-language tasks, they remain susceptible to adversarial attacks. Existing white-box attacks o…
MOKD: Cross-domain Finetuning for Few-shot Classification via Maximizing Optimized Kernel Dependence
Hongduan Tian, Feng Liu, Tongliang Liu +3
In cross-domain few-shot classification, \emph{nearest centroid classifier} (NCC) aims to learn representations to construct a metric space where few-shot classification can be per…