In he past decade, object detection has achieved significant progress in natural images but
not in aerial images, due to the massive variations in the scale and orientation of objects …

This paper focus on few-shot object detection~(FSOD) and instance segmentation~(FSIS),
which requires a model to quickly adapt to novel classes with a few labeled instances. The …

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

Object detection under imperfect data receives great attention recently. Weakly supervised
object detection (WSOD) suffers from severe localization issues due to the lack of instance …

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 …

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

Conventional deep learning based methods for 3D object detection require a large amount
of 3D bounding box annotations for training, which is expensive to obtain in practice …

Despite the importance of unsupervised object detection, to the best of our knowledge, there
is no previous work addressing this problem. One main issue, widely known to the …

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 …

The direct effect of global warming on viticulture is already apparent, with unexpected pests
and diseases as one of the most concerning consequences. Deploying sticky traps on grape …