As an emerging and challenging problem in the computer vision community, weakly
supervised object localization and detection plays an important role for develo** new …

Object detection, as of one the most fundamental and challenging problems in computer
vision, has received great attention in recent years. Over the past two decades, we have …

Current object detectors are limited in vocabulary size due to the small scale of detection
datasets. Image classifiers, on the other hand, reason about much larger vocabularies, as …

Existing open-vocabulary object detectors typically enlarge their vocabulary sizes by
leveraging different forms of weak supervision. This helps generalize to novel objects at …

Object detection is one of the most important and challenging branches of computer vision,
which has been widely applied in people's life, such as monitoring security, autonomous …

Localizing objects in image collections without supervision can help to avoid expensive
annotation campaigns. We propose a simple approach to this problem, that leverages the …

Object detection in remote sensing images (RSIs) requires the locating and classifying of
objects of interest, which is a hot topic in RSI analysis research. With the development of …

Sketches are highly expressive, inherently capturing subjective and fine-grained visual
cues. The exploration of such innate properties of human sketches has, however, been …

Weakly supervised learning has emerged as a compelling tool for object detection by
reducing the need for strong supervision during training. However, major challenges …

Recently, due to the excellent representation ability of convolutional neural networks
(CNNs), object detection in remote sensing images has undergone remarkable …