8 accepted papers
Adapting CLIP to vertical domains is typically approached by novel fine-tuning strategies or by continual pre-training (CPT) on large domain-specific datasets. Yet, data itself remains an underexplored factor in this process. We revisit this task from a data-centric perspective: Can effective data s
Inferring social relationships from users' mobile data holds significant value for personalized recommendations. Most methods model user interactions based on co-occurrence records, achieving impressive success in capturing social signals. However, despite these advancements, current techniques stil
Trajectory representation learning transforms complex spatio-temporal features of trajectories into dense, low-dimensional embeddings, enabling applications in intelligent transportation systems. With advances in this field and the availability of large-scale traffic data, intelligent urban systems
Leveraging review texts to mine deep user preferences is vital for recommendation. However, existing methods neglect the positive-negative counteraction and rely on noisy hard sentiment thresholds. Furthermore, the feature density asymmetry causes dense semantic features to overwhelm sparse collabor
Trajectory representation learning transforms trajectory data into low-dimensional embeddings for downstream analytics. Although trajectory data inherently contains rich spatiotemporal information that remains to be more deeply explored, recent approaches have increasingly favored integrating extern
Multivariate time series forecasting is a critical focus across many fields. Existing transformer-based models have overlooked the explicit modeling of inter-variable correlations. Similarly, the graph-based methods have also failed to address the dynamic nature of multivariate correlations and the
Cross-Corpus Speech Emotion Recognition (SER) aims to identify human emotions from speech across different speakers and languages. Previous work engaged in extracting the domain-invariant features among individual samples that are most relevant to emotions, ignoring rich relationships between speech…
Deep Neural Networks (DNNs) have been criticized because they easily overfit noisy (incorrect) labels. To improve the robustness of DNNs, existing methods for image data regard samples with small training losses as correctly labeled data (small-loss criterion). Nevertheless, time series' discriminat…