172 citations · 410 across the 14 of their papers we have counts for
15 papers
When Semantically Consistent Encoding Meets View-Label Heterogeneity Modeling: A Unified Framework for Incomplete Multi-View Multi-Label Learning
Chengliang Liu, Bo Li, Bob Zhang +3
Incomplete multi-view multi-label learning requires not only robust semantic aggregation from partially observed views, but also label-aware exploitation of view-specific evidence.…
VideoVIBE: A Video-Grounded Diagnostic Benchmark for One-Shot Interactive Website Generation
Jiajun Xu, Yanghao Zhou, Jingyun Liao +6
Natural-language-driven "vibe coding" enables the one-shot generation of visually rich and interactive web applications, yet reliable assessment of their quality has not kept pace.…
OSAGEN: Object-Aware Mask Priors and Multistage Decoupled Diffusion for Industrial Anomaly Generation
Jinyi Xu, Peng Chen, Yunkang Cao +3
Industrial anomaly detection and localization are limited by scarce real anomalies and pixel-level annotations, a bottleneck that synthetic image-mask pairs can alleviate. However,…
Null-Space Constrained Low-Rank Adaptation for Response-Specified Large Language Model Unlearning
Bocheng Ju, Jianhua Wang, Chengliang Liu +1
Large language model unlearning aims to suppress designated undesirable knowledge while preserving benign capabilities. Many unlearning objectives focus on suppressing undesired an…
Generative Data-engine Foundation Model for Universal Few-shot 2D Vascular Image Segmentation
Rongjun Ge, Xin Li, Yuxing Liu +8
The segmentation of 2D vascular structures via deep learning holds significant clinical value but is hindered by the scarcity of annotated data, severely limiting its widespread ap…
Towards Explainable Industrial Anomaly Detection via Knowledge-Guided Latent Reasoning
Peng Chen, Chao Huang, Yunkang Cao +7
Industrial anomaly detection demands precise reasoning over fine-grained defect patterns. However, existing multimodal large language models (MLLMs), pretrained on general-domain d…
IAD-R1: Reinforcing Consistent Reasoning in Industrial Anomaly Detection
Yanhui Li, Yunkang Cao, Chengliang Liu +3
Industrial anomaly detection is a critical component of modern manufacturing, yet the scarcity of defective samples restricts traditional detection methods to scenario-specific app…
Vad-R1: Towards Video Anomaly Reasoning via Perception-to-Cognition Chain-of-Thought
Chao Huang, Benfeng Wang, Jie Wen +4
Recent advancements in reasoning capability of Multimodal Large Language Models (MLLMs) demonstrate its effectiveness in tackling complex visual tasks. However, existing MLLM-based…
Wavelet-based Global-Local Interaction Network with Cross-Attention for Multi-View Diabetic Retinopathy Detection
Yongting Hu, Yuxin Lin, Chengliang Liu +4
Multi-view diabetic retinopathy (DR) detection has recently emerged as a promising method to address the issue of incomplete lesions faced by single-view DR. However, it is still c…
Masked Two-channel Decoupling Framework for Incomplete Multi-view Weak Multi-label Learning
Chengliang Liu, Jie Wen, Yabo Liu +4
Multi-view learning has become a popular research topic in recent years, but research on the cross-application of classic multi-label classification and multi-view learning is stil…
Information Recovery-Driven Deep Incomplete Multiview Clustering Network
Chengliang Liu, Jie Wen, Zhihao Wu +3
Incomplete multi-view clustering is a hot and emerging topic. It is well known that unavoidable data incompleteness greatly weakens the effective information of multi-view data. To…
DICNet: Deep Instance-Level Contrastive Network for Double Incomplete Multi-View Multi-Label Classification
Chengliang Liu, Jie Wen, Xiaoling Luo +3
In recent years, multi-view multi-label learning has aroused extensive research enthusiasm. However, multi-view multi-label data in the real world is commonly incomplete due to the…
Incomplete Multi-View Multi-Label Learning via Label-Guided Masked View- and Category-Aware Transformers
Chengliang Liu, Jie Wen, Xiaoling Luo +1
As we all know, multi-view data is more expressive than single-view data and multi-label annotation enjoys richer supervision information than single-label, which makes multi-view…
Reliable Representation Learning for Incomplete Multi-View Missing Multi-Label Classification
Chengliang Liu, Jie Wen, Yong Xu +3
As a cross-topic of multi-view learning and multi-label classification, multi-view multi-label classification has gradually gained traction in recent years. The application of mult…
Localized Sparse Incomplete Multi-view Clustering
Chengliang Liu, Zhihao Wu, Jie Wen +2
Incomplete multi-view clustering, which aims to solve the clustering problem on the incomplete multi-view data with partial view missing, has received more and more attention in re…