12 papers
LLM-Enhanced Multimodal Fusion for Cross-Domain Sequential Recommendation
Wangyu Wu, Zhenhong Chen, Wenqiao Zhang +5
Cross-Domain Sequential Recommendation (CDSR) predicts user behavior by leveraging historical interactions across multiple domains, focusing on modeling cross-domain preferences an…
Context Patch Fusion With Class Token Enhancement for Weakly Supervised Semantic Segmentation
Yiyang Fu, Hui Li, Wangyu Wu
Weakly Supervised Semantic Segmentation (WSSS), which relies only on image-level labels, has attracted significant attention for its cost-effectiveness and scalability. Existing me…
SynthSeg-Agents: Multi-Agent Synthetic Data Generation for Zero-Shot Weakly Supervised Semantic Segmentation
Wangyu Wu, Zhenhong Chen, Xiaowei Huang +2
Weakly Supervised Semantic Segmentation (WSSS) with image level labels aims to produce pixel level predictions without requiring dense annotations. While recent approaches have lev…
Tag-Enriched Multi-Attention with Large Language Models for Cross-Domain Sequential Recommendation
Wangyu Wu, Xuhang Chen, Zhenhong Chen +5
Cross-Domain Sequential Recommendation (CDSR) plays a crucial role in modern consumer electronics and e-commerce platforms, where users interact with diverse services such as books…
Contrastive Prompt Clustering for Weakly Supervised Semantic Segmentation
Wangyu Wu, Zhenhong Chen, Xiaowen Ma +6
Weakly Supervised Semantic Segmentation (WSSS) with image-level labels has gained attention for its cost-effectiveness. Most existing methods emphasize inter-class separation, ofte…
Cognitive-Inspired Hierarchical Attention Fusion With Visual and Textual for Cross-Domain Sequential Recommendation
Wangyu Wu, Zhenhong Chen, Siqi Song +4
Cross-Domain Sequential Recommendation (CDSR) predicts user behavior by leveraging historical interactions across multiple domains, focusing on modeling cross-domain preferences th…