75 citations · 94 across the 5 of their papers we have counts for
5 papers
MB-HGCN: A Hierarchical Graph Convolutional Network for Multi-behavior Recommendation
Mingshi Yan, Zhiyong Cheng, Jing Sun +2
Collaborative filtering-based recommender systems that rely on a single type of behavior often encounter serious sparsity issues in real-world applications, leading to unsatisfacto…
CLIP-VG: Self-paced Curriculum Adapting of CLIP for Visual Grounding
Linhui Xiao, Xiaoshan Yang, Fang Peng +3
Visual Grounding (VG) is a crucial topic in the field of vision and language, which involves locating a specific region described by expressions within an image. To reduce the reli…
Improved Visual Fine-tuning with Natural Language Supervision
Junyang Wang, Yuanhong Xu, Juhua Hu +3
Fine-tuning a visual pre-trained model can leverage the semantic information from large-scale pre-training data and mitigate the over-fitting problem on downstream vision tasks wit…
Zero-shot Image Captioning by Anchor-augmented Vision-Language Space Alignment
Junyang Wang, Yi Zhang, Ming Yan +2
CLIP (Contrastive Language-Image Pre-Training) has shown remarkable zero-shot transfer capabilities in cross-modal correlation tasks such as visual classification and image retriev…
Cascading Residual Graph Convolutional Network for Multi-Behavior Recommendation
Mingshi Yan, Zhiyong Cheng, Chen Gao +4
Multi-behavior recommendation exploits multiple types of user-item interactions to alleviate the data sparsity problem faced by the traditional models that often utilize only one t…