The agriculture plays vital role in societies and requires research, planning and execution. It
is important to research new trends, scientific methods and boosters that can give a boost it …

Few-shot learning (FSL) methods typically assume clean support sets with accurately
labeled samples when training on novel classes. This assumption can often be unrealistic …

We study the challenging incremental few-shot object detection (iFSD) setting. Recently,
hypernetwork-based approaches have been studied in the context of continuous and …

Cervical cancer is a worldwide public health problem with a high rate of illness and mortality
among women. In this study, we proposed a novel framework based on Faster RCNN-FPN …

Understanding the visual world from the perspective of humans (egocentric) has been a
long-standing challenge in computer vision. Egocentric videos exhibit high scene complexity …

Object detectors usually achieve promising results with the supervision of complete instance
annotations. However, their performance is far from satisfactory with sparse instance …

Positive-unlabeled learning is an essential problem in many real-world applications with
only labeled positive and unlabeled data, especially when the negative samples are difficult …

Fully supervised object detection requires training images in which all instances are
annotated. This is actually impractical due to the high labor and time costs and the …

Semi-supervised domain adaptation (SSDA) adopts a model trained from a label-rich source
domain to a new but related domain with a few labels of target data. It is shown that, in an …

In many applications, such as autonomous driving, hand manipulation, or robot navigation,
object detection methods must be able to detect objects unseen in the training set. Open …