172 citations · 410 across the 14 of their papers we have counts for
12 papers · 1 filter
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,…
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…
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…