22 citations · 23 across the 6 of their papers we have counts for
6 papers · 1 filter
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…
ConsistentRFT: Reducing Visual Hallucinations in Flow-based Reinforcement Fine-Tuning
Xiaofeng Tan, Jun Liu, Yuanting Fan +7
Reinforcement Fine-Tuning (RFT) on flow-based models is crucial for preference alignment. However, they often introduce visual hallucinations like over-optimized details and semant…
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…
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…