Search Results for author:
Found 9 papers, 2 papers with code
Con4m: Context-aware Consistency Learning Framework for Segmented Time Series Classification
Leveraging the contextual priors of MVD at both the data and label levels, we propose a novel consistency learning framework Con4m, which effectively utilizes contextual information more conducive to discriminating consecutive segments in segmented TSC tasks, while harmonizing inconsistent boundary labels for training.
Are Synthetic Time-series Data Really not as Good as Real Data?
Time-series data presents limitations stemming from data quality issues, bias and vulnerabilities, and generalization problem.
MBrain: A Multi-channel Self-Supervised Learning Framework for Brain Signals
Intuitively, brain signals, generated by the firing of neurons, are transmitted among different connecting structures in human brain.
BrainNet: Epileptic Wave Detection from SEEG with Hierarchical Graph Diffusion Learning
Therefore, we propose the first data-driven study to detect epileptic waves in a real-world SEEG dataset.
Style Variable and Irrelevant Learning for Generalizable Person Re-identification
In this paper, we first verify through an experiment that style factors are a vital part of domain bias.
Symmetric Network with Spatial Relationship Modeling for Natural Language-based Vehicle Retrieval
Natural language (NL) based vehicle retrieval aims to search specific vehicle given text description.
Vehicle Re-identification Method Based on Vehicle Attribute and Mutual Exclusion Between Cameras
Vehicle Re-identification aims to identify a specific vehicle across time and camera view.
Unsupervised Adversarially-Robust Representation Learning on Graphs
Additionally, we explore a provable connection between the robustness of the unsupervised graph encoder and that of models on downstream tasks.
Attribute-guided Feature Extraction and Augmentation Robust Learning for Vehicle Re-identification
Vehicle re-identification is one of the core technologies of intelligent transportation systems and smart cities, but large intra-class diversity and inter-class similarity poses great challenges for existing method.
