6 accepted papers
Cross-subject generalization in biomedical time-series (BTS) refers to training on data from some subjects and testing on unseen subjects. The key challenge is to suppress subject-specific variability in BTS representations. Most existing methods implicitly suppress the variability through model bui…
Electroencephalography (EEG) is a time-series signal containing semantic information that can be used to determine human brain activities. Artifacts within EEG data can interfere with the intrinsic distribution of this semantic information, so removing artifacts is crucial for improving EEG analysis…
Multimodal dialogue emotion recognition integrates data from multiple modalities to accurately identify emotional states in conversations. However, differences in expression and information density across modalities complicate the fusion of features. Traditional methods may introduce redundant infor…
POI representation learning plays a crucial role in handling tasks related to user mobility data. Recent studies have shown that enriching POI representations with multimodal information can significantly enhance their task performance. Previously, the textual information incorporated into POI repr…
Data augmentation effectively expands feature distribution in time series classification, enhancing downstream task performance. However, existing techniques often fail to maintain semantic consistency between augmented and original time series data, causing label noise and thereby degrading downstr…
Human motion prediction (HMP) predicts future human pose sequences given the past ones. HMP has recently attracted attention in computer vision and the robotics domain as it helps machines understand human behavior, plan target actions and optimize interaction strategies. Existing methods for HMP ar