activity
20192025
most citedHS-GCN: Hamming Spatial Graph Convolutional Networks for Recommendation

54 citations · 136 across the 9 of their papers we have counts for

collaborators

12 papers

cs.CV2025

Self-Enhanced Image Clustering with Cross-Modal Semantic Consistency

Zihan Li, Wei Sun, Jing Hu +3

While large language-image pre-trained models like CLIP offer powerful generic features for image clustering, existing methods typically freeze the encoder. This creates a fundamen…

cs.CL2025

An Enhanced Model-based Approach for Short Text Clustering

Enhao Cheng, Shoujia Zhang, Jianhua Yin +3

Short text clustering has become increasingly important with the popularity of social media like Twitter, Google+, and Facebook. Existing methods can be broadly categorized into tw…

cs.LG2025

Dual-Center Graph Clustering with Neighbor Distribution

Enhao Cheng, Shoujia Zhang, Jianhua Yin +2

Graph clustering is crucial for unraveling intricate data structures, yet it presents significant challenges due to its unsupervised nature. Recently, goal-directed clustering tech…

cs.AI2025★ 2 cited

Mitigating Hallucination Through Theory-Consistent Symmetric Multimodal Preference Optimization

Wenqi Liu, Xuemeng Song, Jiaxi Li +4

Direct Preference Optimization (DPO) has emerged as an effective approach for mitigating hallucination in Multimodal Large Language Models (MLLMs). Although existing methods have a…

cs.MM2023★ 28 cited

Self-Training Boosted Multi-Factor Matching Network for Composed Image Retrieval

Haokun Wen, Xuemeng Song, Jianhua Yin +3

The composed image retrieval (CIR) task aims to retrieve the desired target image for a given multimodal query, i.e., a reference image with its corresponding modification text. Th…

cs.IR2023★ 54 cited

HS-GCN: Hamming Spatial Graph Convolutional Networks for Recommendation

Han Liu, Yinwei Wei, Jianhua Yin +1

An efficient solution to the large-scale recommender system is to represent users and items as binary hash codes in the Hamming space. Towards this end, existing methods tend to co…