Self-supervised learning (SSL) has recently achieved impressive performance on various
time series tasks. The most prominent advantage of SSL is that it reduces the dependence …

The success of existing multi-view clustering methods heavily relies on the assumption of
view consistency and instance completeness, referred to as the complete information …

The quality of the data and annotation upper-bounds the quality of a downstream model.
While there exist large text corpora and image-text pairs high-quality video-text data is much …

Although existing semi-supervised learning models achieve remarkable success in learning
with unannotated in-distribution data, they mostly fail to learn on unlabeled data sampled …

This study reveals the inherent tolerance of contrastive learning (CL) towards sampling bias,
wherein negative samples may encompass similar semantics (\eg labels). However, existing …

Existing deep clustering methods rely on either contrastive or non-contrastive representation
learning for downstream clustering task. Contrastive-based methods thanks to negative …

Weakly-supervised temporal action localization aims to localize actions in untrimmed videos
with only video-level labels. Most existing methods address this problem with a" localization …

Medical datasets and especially biobanks, often contain extensive tabular data with rich
clinical information in addition to images. In practice, clinicians typically have less data, both …

Negative sampling has swiftly risen to prominence as a focal point of research, with wide-
ranging applications spanning machine learning, computer vision, natural language …

Self-supervised audio-visual source localization aims to locate sound-source objects in
video frames without extra annotations. Recent methods often approach this goal with the …