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

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 …

Recent trends in semi-supervised learning have significantly boosted the performance of 3D
semi-supervised medical image segmentation. Compared with 2D images, 3D medical …

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 …

Active learning selects informative samples for annotation within budget, which has proven
efficient recently on object detection. However, the widely used active detection benchmarks …

Most of the recent research in semi-supervised object detection follows the pseudo-labeling
paradigm evolved from the semi-supervised image classification task. However, the training …

This paper focuses on long-tailed object detection in the semi-supervised learning setting,
which poses realistic challenges, but has rarely been studied in the literature. We propose a …

Autonomous vehicle (AV) systems rely on robust perception models as a cornerstone of
safety assurance. However objects encountered on the road exhibit a long-tailed distribution …

Abstract Referring Expression Comprehension (REC) is a task of grounding the referent
based on an expression, and its development is greatly limited by expensive instance-level …

Audio-visual segmentation (AVS) aims to segment the sounding objects in video frames.
Although great progress has been witnessed we experimentally reveal that current methods …