Deep clustering can optimize representations of instances (ie, representation learning) and
explore the inherent data distribution (ie, clustering) simultaneously, which demonstrates a …

Confidence-based pseudo-labeling is among the dominant approaches in semi-supervised
learning (SSL). It relies on including high-confidence predictions made on unlabeled data as …

Multiple clustering has gained significant attention in recent years due to its potential to
reveal multiple hidden structures of data from different perspectives. The advent of deep …

Although supervised learning has been highly successful in improving the state-of-the-art in
the domain of image-based computer vision in the past, the margin of improvement has …

In this paper, we propose a genuine group-level contrastive visual representation learning
method whose linear evaluation performance on ImageNet surpasses the vanilla supervised …

Modern image retrieval methods typically rely on fine-tuning pre-trained encoders to extract
image-level descriptors. However, the most widely used models are pre-trained on …

Previous endeavors in self-supervised learning have enlightened the research of deep
clustering from an instance discrimination perspective. Built upon this foundation, recent …

Fine-tuning a visual pre-trained model can leverage the semantic information from large-
scale pre-training data and mitigate the over-fitting problem on downstream vision tasks with …

With the increasing prevalence of digital multimedia devices and the growing reliance on
compression and wireless data transmission, evaluating image quality remains a persistent …

Mining users' intents plays a crucial role in sequential recommendation. The recent
approach, ICLRec, was introduced to extract underlying users' intents using contrastive …