19 citations · 55 across the 21 of their papers we have counts for
21 papers
PairCFR: Enhancing Model Training on Paired Counterfactually Augmented Data through Contrastive Learning
Xiaoqi Qiu, Yongjie Wang, Xu Guo +4
Counterfactually Augmented Data (CAD) involves creating new data samples by applying minimal yet sufficient modifications to flip the label of existing data samples to other classe…
DivAvatar: Diverse 3D Avatar Generation with a Single Prompt
Weijing Tao, Biwen Lei, Kunhao Liu +4
Text-to-Avatar generation has recently made significant strides due to advancements in diffusion models. However, most existing work remains constrained by limited diversity, produ…
Disentangled Graph Variational Auto-Encoder for Multimodal Recommendation with Interpretability
Xin Zhou, Chunyan Miao
Multimodal recommender systems amalgamate multimodal information (e.g., textual descriptions, images) into a collaborative filtering framework to provide more accurate recommendati…
HGAttack: Transferable Heterogeneous Graph Adversarial Attack
He Zhao, Zhiwei Zeng, Yongwei Wang +2
Heterogeneous Graph Neural Networks (HGNNs) are increasingly recognized for their performance in areas like the web and e-commerce, where resilience against adversarial attacks is…
Rethinking Negative Pairs in Code Search
Haochen Li, Xin Zhou, Luu Anh Tuan +1
Recently, contrastive learning has become a key component in fine-tuning code search models for software development efficiency and effectiveness. It pulls together positive code s…
A Knowledge-Driven Cross-view Contrastive Learning for EEG Representation
Weining Weng, Yang Gu, Qihui Zhang +3
Due to the abundant neurophysiological information in the electroencephalogram (EEG) signal, EEG signals integrated with deep learning methods have gained substantial traction acro…