4 accepted papers
Disentanglement-based methods for learning shared representations are widely used in multimodal sentiment analysis. However, existing methods often overlook potential emotional conflicts across modalities within the same sample. They typically adopt intra-modal reconstruction and rely on similarity
Existing early and late fusion frameworks in multimodal learning are confronted with the fundamental challenge of modality imbalance, wherein disparities in representational capacities induce inter-modal competition during training. Current research methodologies primarily rely on modality-level con…
Cold-start sequential recommendation, where user interaction histories are sparse or minimal, remains a significant challenge in recommendation systems. Current meta-learning-based approaches rely heavily on the interaction histories of regular users to construct meta-tasks, aiming to acquire prior…
The success of current graph contrastive learning methods largely relies on the choice of data augmentation and contrastive objectives. However, most existing methods tend to optimize these two components independently, neglecting their potential interplay, which leads to suboptimal quality of the l