Cell segmentation is a critical step for quantitative single-cell analysis in microscopy images.
Existing cell segmentation methods are often tailored to specific modalities or require …

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 …

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 …

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 …

Semi-Supervised Object Detection (SSOD) has been successful in improving the
performance of both R-CNN series and anchor-free detectors. However, one-stage anchor …

Object detection (OD), a crucial vision task, remains challenged by the lack of large training
datasets with precise object localization labels. In this work, we propose ALWOD, a new …

Scale variation across object instances is one of the key challenges in object detection.
Although modern detection models have achieved remarkable progress in dealing with the …

Abstract State-of-the-art 3D object detectors are usually trained on large-scale datasets with
high-quality 3D annotations. However, such 3D annotations are often expensive and time …

Cross-domain ship detection tries to identify synthetic aperture radar (SAR) ships by
adapting knowledge from labeled optical images, without labor-intensive annotations. In …