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 …

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 …

We analyze the DETR-based framework on semi-supervised object detection (SSOD) and
observe that (1) the one-to-one assignment strategy generates incorrect matching when the …

Abstract Semi-Supervised Object Detection (SSOD), aiming to explore unlabeled data for
boosting object detectors, has become an active task in recent years. However, existing …

Abstract With basic Semi-Supervised Object Detection (SSOD) techniques, one-stage
detectors generally obtain limited promotions compared with two-stage clusters. We …

To date, the most powerful semi-supervised object detectors (SS-OD) are based on pseudo-
boxes, which need a sequence of post-processing with fine-tuned hyper-parameters. In this …

Due to the costliness of labelled data in real-world applications, semi-supervised learning,
underpinned by pseudo labelling, is an appealing solution. However, handling confusing …

In this paper we address the limitations of the DETR-based semi-supervised object detection
(SSOD) framework particularly focusing on the challenges posed by the quality of object …

In this paper, we delve into two key techniques in Semi-Supervised Object Detection
(SSOD), namely pseudo labeling and consistency training. We observe that these two …

Semi-supervised object detection has made significant progress with the development of
mean teacher driven self-training. Despite the promising results, the label mismatch problem …