22 citations · 29 across the 17 of their papers we have counts for
13 papers · 1 filter
When Policy Entropy Constraint Fails: Preserving Diversity in Flow-based RLHF via Perceptual Entropy
Xiaofeng Tan, Jun Liu, Bin-Bin Gao +5
RLHF is widely used to align flow-matching text-to-image models with human preferences, but often leads to severe diversity collapse after fine-tuning. In RL, diversity is often as…
AnomalyClaw: A Universal Visual Anomaly Detection Agent via Tool-Grounded Refutation
Xi Jiang, Yinjie Zhao, Zesheng Yang +1
Visual anomaly detection (VAD) is crucial in many real-world fields, such as industrial inspection, medical imaging, infrastructure monitoring, and remote sensing. However, the spe…
AD-Copilot: A Vision-Language Assistant for Industrial Anomaly Detection via Visual In-context Comparison
Xi Jiang, Yue Guo, Jian Li +7
Multimodal Large Language Models (MLLMs) have achieved impressive success in natural visual understanding, yet they consistently underperform in industrial anomaly detection (IAD).…
Mastering Negation: Boosting Grounding Models via Grouped Opposition-Based Learning
Zesheng Yang, Xi Jiang, Bingzhang Hu +4
Current vision-language detection and grounding models predominantly focus on prompts with positive semantics and often struggle to accurately interpret and ground complex expressi…
Dual-Grained Policy Optimization for Hallucination Reduction in Flow-Based RL
Xiaofeng Tan, Jun Liu, Yuanting Fan +7
Reinforcement Fine-Tuning (RFT) on flow-based models is crucial for alignment. However, they often introduce visual hallucinations like over-optimized details and semantic misalign…
UniPCB: A Unified Vision-Language Benchmark for Open-Ended PCB Quality Inspection
Fuxiang Sun, Xi Jiang, Jiansheng Wu +3
Multimodal Large Language Models (MLLMs) show promise for general industrial quality inspection, but fall short in complex scenarios, such as Printed Circuit Board (PCB) inspection…
SoftPatch+: Fully Unsupervised Anomaly Classification and Segmentation
Chengjie Wang, Xi Jiang, Bin-Bin Gao +4
Although mainstream unsupervised anomaly detection (AD) (including image-level classification and pixel-level segmentation)algorithms perform well in academic datasets, their perfo…
Transferability of Adversarial Attacks in Video-based MLLMs: A Cross-modal Image-to-Video Approach
Linhao Huang, Xue Jiang, Zhiqiang Wang +5
Video-based multimodal large language models (V-MLLMs) have shown vulnerability to adversarial examples in video-text multimodal tasks. However, the transferability of adversarial…
CAR: Controllable Autoregressive Modeling for Visual Generation
Ziyu Yao, Jialin Li, Yifeng Zhou +6
Controllable generation, which enables fine-grained control over generated outputs, has emerged as a critical focus in visual generative models. Currently, there are two primary te…
Toward Multi-class Anomaly Detection: Exploring Class-aware Unified Model against Inter-class Interference
Xi Jiang, Ying Chen, Qiang Nie +4
In the context of high usability in single-class anomaly detection models, recent academic research has become concerned about the more complex multi-class anomaly detection. Altho…
Tuning-Free Image Customization with Image and Text Guidance
Pengzhi Li, Qiang Nie, Ying Chen +7
Despite significant advancements in image customization with diffusion models, current methods still have several limitations: 1) unintended changes in non-target areas when regene…
Unsupervised Continual Anomaly Detection with Contrastively-learned Prompt
Jiaqi Liu, Kai Wu, Qiang Nie +6
Unsupervised Anomaly Detection (UAD) with incremental training is crucial in industrial manufacturing, as unpredictable defects make obtaining sufficient labeled data infeasible. H…
Beyond Prototypes: Semantic Anchor Regularization for Better Representation Learning
Yanqi Ge, Qiang Nie, Ye Huang +5
One of the ultimate goals of representation learning is to achieve compactness within a class and well-separability between classes. Many outstanding metric-based and prototype-bas…