Partial label learning (PLL) is an emerging framework in weakly supervised machine
learning with broad application prospects. It handles the case in which each training …

Existing vision-text contrastive learning like CLIP (Radford et al., 2021) aims to match the
paired image and caption embeddings while pushing others apart, which improves …

Detecting out-of-distribution (OOD) objects is indispensable for safely deploying object
detectors in the wild. Although distance-based OOD detection methods have demonstrated …

Partial label learning (PLL), which refers to the classification task where each training
instance is ambiguously annotated with a set of candidate labels, has been recently studied …

Machine learning models deployed in the wild naturally encounter unlabeled samples from
both known and novel classes. Challenges arise in learning from both the labeled and …

Under partial-label learning (PLL) where, for each training instance, only a set of ambiguous
candidate labels containing the unknown true label is accessible, contrastive learning has …

Partial-label learning (PLL) is a peculiar weakly-supervised learning task where the training
samples are generally associated with a set of candidate labels instead of single ground …

Weakly supervised whole slide image classification is usually formulated as a multiple
instance learning (MIL) problem, where each slide is treated as a bag, and the patches cut …

Vision-language models, such as contrastive language-image pre-training (CLIP), have
demonstrated impressive results in natural image domains. However, these models often …

Real-world data usually couples the label ambiguity and heavy imbalance, challenging the
algorithmic robustness of partial label learning (PLL) and long-tailed learning (LT). The …