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 …

Supervised learning based object detection frameworks demand plenty of laborious manual
annotations, which may not be practical in real applications. Semi-supervised object …

We propose a semi-supervised approach for contemporary object detectors following the
teacher-student dual model framework. Our method is featured with 1) the exponential …

In this paper, we study teacher-student learning from the perspective of data initialization
and propose a novel algorithm called Active Teacher for semi-supervised object detection …

In this study, we dive deep into the inconsistency of pseudo targets in semi-supervised
object detection (SSOD). Our core observation is that the oscillating pseudo-targets …

We present GraphMix, a regularization method for Graph Neural Network based semi-
supervised object classification, whereby we propose to train a fully-connected network …

In this paper, we delve into semi-supervised object detection where unlabeled images are
leveraged to break through the upper bound of fully-supervised object detection models …

Imposing consistency through proxy tasks has been shown to enhance data-driven learning
and enable self-supervision in various tasks. This paper introduces novel and effective …

Many recent semi-supervised learning (SSL) studies build teacher-student architecture and
train the student network by the generated supervisory signal from the teacher. Data …

In this work, we focus on semi-supervised learning for video action detection which utilizes
both labeled as well as unlabeled data. We propose a simple end-to-end consistency based …