Deep learning algorithms are a subset of the machine learning algorithms, which aim at
discovering multiple levels of distributed representations. Recently, numerous deep learning …

Over the years, advances in sensor technologies have enhanced spatial, temporal, spectral,
and radiometric resolutions, thus significantly improving the size, resolution, and quality of …

摘要目标检测的任务是从图像中精确且高效地识别, 定位出大量预定义类别的物体实例.
随着深度学习的广泛应用, 目标检测的精确度和效率都得到了较大提升, 但基于深度学习的目标 …

Despite the remarkable accuracy of deep neural networks in object detection, they are costly
to train and scale due to supervision requirements. Particularly, learning more object …

Object detection, one of the most fundamental and challenging problems in computer vision,
seeks to locate object instances from a large number of predefined categories in natural …

Deep domain adaptation has emerged as a new learning technique to address the lack of
massive amounts of labeled data. Compared to conventional methods, which learn shared …

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 …

Domain adaptation studies learning algorithms that generalize across source domains and
target domains that exhibit different distributions. Recent studies reveal that deep neural …

Adversarial learning has been embedded into deep networks to learn disentangled and
transferable representations for domain adaptation. Existing adversarial domain adaptation …

Recent advances in deep domain adaptation reveal that adversarial learning can be
embedded into deep networks to learn transferable features that reduce distribution …