3 accepted papers
Real-world data is frequently noisy and ambiguous. In crowdsourcing, for example, human annotators may assign conflicting class labels to the same instances. Partial-label learning (PLL) addresses this challenge by training classifiers when each instance is associated with a set of candidate labels,…
Deep learning-based trajectory prediction models have demonstrated promising capabilities in capturing complex interactions. However, their generalization beyond the available training data, limited in both size and diversity, remains a significant challenge. To improve generalization, we propose SH…
Object detectors in real-world applications often fail to detect objects due to varying factors such as weather conditions and noisy input. Therefore, a process that mitigates false detections is crucial for both safety and accuracy. While uncertainty-based thresholding shows promise, previous works…