44 citations · 52 across the 13 of their papers we have counts for
13 papers
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…
MAC-Lookup: Multi-Axis Conditional Lookup Model for Underwater Image Enhancement
Fanghai Yi, Zehong Zheng, Zexiao Liang +4
Enhancing underwater images is crucial for exploration. These images face visibility and color issues due to light changes, water turbidity, and bubbles. Traditional prior-based me…
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…
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 Fusion for Cross-Domain Sequential Recommendation
Wangyu Wu, Siqi Song, Xianglin Qiu +3
Cross-Domain Sequential Recommendation (CDSR) aims to predict future user interactions based on historical interactions across multiple domains. The key challenge in CDSR is effect…
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…
Adaptive Patch Contrast for Weakly Supervised Semantic Segmentation
Wangyu Wu, Tianhong Dai, Zhenhong Chen +4
Weakly Supervised Semantic Segmentation (WSSS) using only image-level labels has gained significant attention due to its cost-effectiveness. The typical framework involves using im…
Top-K Pooling with Patch Contrastive Learning for Weakly-Supervised Semantic Segmentation
Wangyu Wu, Tianhong Dai, Xiaowei Huang +2
Weakly Supervised Semantic Segmentation (WSSS) using only image-level labels has gained significant attention due to cost-effectiveness. Recently, Vision Transformer (ViT) based me…
Image Augmentation with Controlled Diffusion for Weakly-Supervised Semantic Segmentation
Wangyu Wu, Tianhong Dai, Xiaowei Huang +2
Weakly-supervised semantic segmentation (WSSS), which aims to train segmentation models solely using image-level labels, has achieved significant attention. Existing methods primar…