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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…
Image Augmentation Agent for Weakly Supervised Semantic Segmentation
Wangyu Wu, Xianglin Qiu, Siqi Song +4
Weakly-supervised semantic segmentation (WSSS) has achieved remarkable progress using only image-level labels. However, most existing WSSS methods focus on designing new network st…
Prompt Categories Cluster for Weakly Supervised Semantic Segmentation
Wangyu Wu, Xianglin Qiu, Siqi Song +3
Weakly Supervised Semantic Segmentation (WSSS), which leverages image-level labels, has garnered significant attention due to its cost-effectiveness. The previous methods mainly st…