Unlike previous studies on the Metaverse based on Second Life, the current Metaverse is
based on the social value of Generation Z that online and offline selves are not different …

Current object detectors are limited in vocabulary size due to the small scale of detection
datasets. Image classifiers, on the other hand, reason about much larger vocabularies, as …

In this work, we revisit the weak-to-strong consistency framework, popularized by FixMatch
from semi-supervised classification, where the prediction of a weakly perturbed image …

Semi-supervised learning (SSL) improves model generalization by leveraging massive
unlabeled data to augment limited labeled samples. However, currently, popular SSL …

We address the task of domain adaptation in object detection, where there is a domain gap
between a domain with annotations (source) and a domain of interest without annotations …

Semi-supervised action recognition is a challenging but critical task due to the high cost of
video annotations. Existing approaches mainly use convolutional neural networks, yet …

With the recent development of Semi-Supervised Object Detection (SS-OD) techniques,
object detectors can be improved by using a limited amount of labeled data and abundant …

State-of-the-art deep learning models are often trained with a large amount of costly labeled
training data. However, requiring exhaustive manual annotations may degrade the model's …

Localizing objects in image collections without supervision can help to avoid expensive
annotation campaigns. We propose a simple approach to this problem, that leverages the …

Pseudo-label-based semi-supervised learning (SSL) has achieved great success on raw
data utilization. However, its training procedure suffers from confirmation bias due to the …