Curriculum learning (CL) is a training strategy that trains a machine learning model from
easier data to harder data, which imitates the meaningful learning order in human curricula …

Training machine learning models in a meaningful order, from the easy samples to the hard
ones, using curriculum learning can provide performance improvements over the standard …

Annotating a qualitative large-scale facial expression dataset is extremely difficult due to the
uncertainties caused by ambiguous facial expressions, low-quality facial images, and the …

The challenge of unsupervised person re-identification (ReID) lies in learning discriminative
features without true labels. This paper formulates unsupervised person ReID as a multi …

Deep neural networks have been shown to be very powerful modeling tools for many
supervised learning tasks involving complex input patterns. However, they can also easily …

Recent deep networks are capable of memorizing the entire data even when the labels are
completely random. To overcome the overfitting on corrupted labels, we propose a novel …

We present a self-supervised learning approach to learn audio-visual representations from
video and audio. Our method uses contrastive learning for cross-modal discrimination of …

The superiority of deeply learned pedestrian representations has been reported in very
recent literature of person re-identification (re-ID). In this article, we consider the more …

We focus on the one-shot learning for video-based person re-Identification (re-ID).
Unlabeled tracklets for the person re-ID tasks can be easily obtained by pre-processing …

Recent success of pre-trained language models crucially hinges on fine-tuning them on
large amounts of labeled data for the downstream task, that are typically expensive to …