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Found 6 papers, 4 papers with code
DeepInteraction++: Multi-Modality Interaction for Autonomous Driving
To demonstrate the effectiveness of the proposed strategy, we design DeepInteraction++, a multi-modal interaction framework characterized by a multi-modal representational interaction encoder and a multi-modal predictive interaction decoder.
Correlation-Distance Graph Learning for Treatment Response Prediction from rs-fMRI
To overcome that, we propose a graph learning framework that captures comprehensive features by integrating both correlation and distance-based similarity measures under a contrastive loss.
MEMD-ABSA: A Multi-Element Multi-Domain Dataset for Aspect-Based Sentiment Analysis
Aspect-based sentiment analysis is a long-standing research interest in the field of opinion mining, and in recent years, researchers have gradually shifted their focus from simple ABSA subtasks to end-to-end multi-element ABSA tasks.
Few-shot Open-set Recognition Using Background as Unknowns
First, instead of learning the decision boundaries between seen classes, as is done in standard close-set classification, we reserve space for unseen classes, such that images located in these areas are recognized as the unseen classes.
Few-Shot Incremental Learning with Continually Evolved Classifiers
First, we adopt a simple but effective decoupled learning strategy of representations and classifiers that only the classifiers are updated in each incremental session, which avoids knowledge forgetting in the representations.
Ranked #8 on
Few-Shot Class-Incremental Learning
on CIFAR-100
Low-Rank Tensor Completion: A Pseudo-Bayesian Learning Approach
Low rank tensor completion, which solves a linear inverse problem with the principle of parsimony, is a powerful technique used in many application domains in computer vision and pattern recognition.
