5 papers
Learning to Adapt Category Consistent Meta-Feature of CLIP for Few-Shot Classification
Jiaying Shi, Xuetong Xue, Shenghui Xu
The recent CLIP-based methods have shown promising zero-shot and few-shot performance on image classification tasks. Existing approaches such as CoOp and Tip-Adapter only focus on…
Learning Positional Attention for Sequential Recommendation
Fan Luo, Haibo He, Juan Zhang +1
Self-attention-based networks have achieved remarkable performance in sequential recommendation tasks. A crucial component of these models is positional encoding. In this study, we…
Heterogeneous Graph-based Framework with Disentangled Representations Learning for Multi-target Cross Domain Recommendation
Xiaopeng Liu, Juan Zhang, Chongqi Ren +3
CDR (Cross-Domain Recommendation), i.e., leveraging information from multiple domains, is a critical solution to data sparsity problem in recommendation system. The majority of pre…
Multi-Granularity and Multi-modal Feature Interaction Approach for Text Video Retrieval
Wenjun Li, Shudong Wang, Dong Zhao +3
The key of the text-to-video retrieval (TVR) task lies in learning the unique similarity between each pair of text (consisting of words) and video (consisting of audio and image fr…
Controllable Talking Face Generation by Implicit Facial Keypoints Editing
Dong Zhao, Jiaying Shi, Wenjun Li +3
Audio-driven talking face generation has garnered significant interest within the domain of digital human research. Existing methods are encumbered by intricate model architectures…