22 citations · 25 across the 14 of their papers we have counts for
14 papers
Breaking the Generator Barrier: Disentangled Representation for Generalizable AI-Text Detection
Xiao Pu, Zepeng Cheng, Lin Yuan +2
As large language models (LLMs) generate text that increasingly resembles human writing, the subtle cues that distinguish AI-generated content from human-written content become inc…
Combating Pattern and Content Bias: Adversarial Feature Learning for Generalized AI-Generated Image Detection
Haifeng Zhang, Qinghui He, Xiuli Bi +3
In recent years, the rapid development of generative artificial intelligence technology has significantly lowered the barrier to creating high-quality fake images, posing a serious…
Leveraging Arbitrary Data Sources for AI-Generated Image Detection Without Sacrificing Generalization
Qinghui He, Haifeng Zhang, Yanan Luo +4
The accelerating advancement of generative models has introduced new challenges for detecting AI-generated images, especially in real-world scenarios where novel generation techniq…
Select, Hypothesize and Verify: Towards Verified Neuron Concept Interpretation
ZeBin Ji, Yang Hu, Xiuli Bi +2
It is essential for understanding neural network decisions to interpret the functionality (also known as concepts) of neurons. Existing approaches describe neuron concepts by gener…
When Detectors Forget Forensics: Blocking Semantic Shortcuts for Generalizable AI-Generated Image Detection
Chao Shuai, Shaojing Fan, Chenlin Zou +6
The growing realism of generative models has blurred the boundary between real and synthetic content, posing significant challenges to reliable AI-generated image detection. Althou…
SSR: Semantic and Spatial Rectification for CLIP-based Weakly Supervised Segmentation
Xiuli Bi, Die Xiao, Junchao Fan +1
In recent years, Contrastive Language-Image Pretraining (CLIP) has been widely applied to Weakly Supervised Semantic Segmentation (WSSS) tasks due to its powerful cross-modal seman…
TGDD: Trajectory Guided Dataset Distillation with Balanced Distribution
Fengli Ran, Xiao Pu, Bo Liu +2
Dataset distillation compresses large datasets into compact synthetic ones to reduce storage and computational costs. Among various approaches, distribution matching (DM)-based met…
Clear Nights Ahead: Towards Multi-Weather Nighttime Image Restoration
Yuetong Liu, Yunqiu Xu, Yang Wei +2
Restoring nighttime images affected by multiple adverse weather conditions is a practical yet under-explored research problem, as multiple weather conditions often coexist in the r…
Mobius: Text to Seamless Looping Video Generation via Latent Shift
Xiuli Bi, Jianfei Yuan, Bo Liu +4
We present Mobius, a novel method to generate seamlessly looping videos from text descriptions directly without any user annotations, thereby creating new visual materials for the…
CustomTTT: Motion and Appearance Customized Video Generation via Test-Time Training
Xiuli Bi, Jian Lu, Bo Liu +4
Benefiting from large-scale pre-training of text-video pairs, current text-to-video (T2V) diffusion models can generate high-quality videos from the text description. Besides, give…
ZeroPur: Succinct Training-Free Adversarial Purification
Erhu Liu, Zonglin Yang, Bo Liu +4
Adversarial purification is a kind of defense technique that can defend against various unseen adversarial attacks without modifying the victim classifier. Existing methods often d…
Detecting Generated Images by Real Images Only
Xiuli Bi, Bo Liu, Fan Yang +4
As deep learning technology continues to evolve, the images yielded by generative models are becoming more and more realistic, triggering people to question the authenticity of ima…
Color-related Local Binary Pattern: A Learned Local Descriptor for Color Image Recognition
Bin Xiao, Tao Geng, Xiuli Bi +1
Local binary pattern (LBP) as a kind of local feature has shown its simplicity, easy implementation and strong discriminating power in image recognition. Although some LBP variants…
D-Unet: A Dual-encoder U-Net for Image Splicing Forgery Detection and Localization
Bo Liu, Ranglei Wu, Xiuli Bi +4
Recently, many detection methods based on convolutional neural networks (CNNs) have been proposed for image splicing forgery detection. Most of these detection methods focus on the…