In this paper, we propose an online clustering method called Contrastive Clustering (CC)
which explicitly performs the instance-and cluster-level contrastive learning. To be specific …

Unsupervised representation learning approaches aim to learn discriminative feature
representations from unlabeled data, without the requirement of annotating every sample …

This work summarizes two strategies for completing time-series (TS) tasks using today's
language model (LLM): LLM-for-TS, design and train a fundamental large model for TS data; …

In this paper, we introduce a novel self-supervised visual representation learning method
which understands both images and videos in a joint learning fashion. The proposed neural …

Learning semantic-rich representations from raw unlabeled time series data is critical for
downstream tasks such as classification and forecasting. Contrastive learning has recently …

Video recognition has been advanced in recent years by benchmarks with rich annotations.
However, research is still mainly limited to human action or sports recognition-focusing on a …

Existing facial expression recognition (FER) methods train encoders with different large-
scale training data for specific FER applications. In this paper, we propose a new task in this …

Self-supervised methods have significantly closed the gap with end-to-end supervised
learning for image classification [13, 24]. In the case of human action videos, however …

With the diversity of information acquisition, data is stored and transmitted in an increasing
number of modalities. Nevertheless, it is not unusual for parts of the data to be lost in some …

We study the problem of supervised contrastive (SupCon) learning on graphs. The SupCon
loss has been recently proposed for classification tasks by pulling data points in the same …