5 accepted papers
Graph neural network (GNN) is a powerful approach for graph-based recommender systems. Recently, GNNs integrated with contrastive learning have achieved superior performance in recommendations due to their data augmentation techniques for sparse data. These methods are susceptible to noisy perturbat…
Vision-language pre-training (VLP) models leverage large-scale cross-modal pre-training to align vision and text modalities, achieving impressive performance on tasks like image-text retrieval and visual grounding. However, these models are highly vulnerable to adversarial attacks, raising critical…
The long-standing one-to-many issue of the open-domain dialogues poses significant challenges for automatic evaluation methods, i.e., there may be multiple suitable responses which differ in semantics for a given conversational context. To tackle this challenge, we propose a novel learning-based aut…
Determining the genres of a trailer is a challenging multi-label classification task. Previous studies tend to classify by CNN or RNN. Recently, Transformer based on attention mechanism has achieved better results in many research fields than CNN and RNN. Inspired by these, we propose a Hierarchical…
Relation classification is to determine the semantic relationship between two entities in a given sentence. However, many relation classifiers are vulnerable to adversarial attacks, which is using adversarial examples to lead victim models to output wrong results. In this paper, we propose a simple…