Referring image segmentation aims to segment a referent via a natural linguistic expression.
Due to the distinct data properties between text and image, it is challenging for a network to …

Learning from noisy data is a challenging task that significantly degenerates the model
performance. In this paper, we present TCL, a novel twin contrastive learning model to learn …

In label-noise learning, the noise transition matrix, bridging the class posterior for noisy and
clean data, has been widely exploited to learn statistically consistent classifiers. The …

This work targets a novel text-driven whole-body motion generation task, which takes a
given textual description as input and aims at generating high-quality, diverse, and coherent …

Learning with noisy labels (LNL) aims to ensure model generalization given a label-
corrupted training set. In this work, we investigate a rarely studied scenario of LNL on fine …

The collection of large-scale datasets inevitably introduces noisy labels, leading to a
substantial degradation in the performance of deep neural networks (DNNs). Although …

Deep regression models typically learn in an end-to-end fashion without explicitly
emphasizing a regression-aware representation. Consequently, the learned representations …

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 …

Although data-driven fault diagnosis methods have achieved remarkable results, these
achievements often rely on high-quality datasets without noisy labels, which can mislead the …

Deep neural networks (DNNs) involve advanced image processing but depend on large
quantities of high-quality labeled data. The presence of noisy data significantly degrades the …