Automatic identification and mapping of tree species is an essential task in forestry and
conservation. However, applications that can geolocate individual trees and identify their …

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 deals with the problem of localizing objects in image and video datasets from
visual exemplars. In particular, we focus on the challenging problem of egocentric visual …

Object detection is one of the most promising research topics currently, whose application in
agriculture, however, can be challenged by the difficulty of annotating complex and crowded …

Diabetic retinopathy (DR) grading from fundus images has attracted increasing interest in
both academic and industrial communities. Most convolutional neural network-based …

Object detection models have become the current tool of choice for plant disease detection
in precision agriculture. Most existing research improved the performance by ameliorating …

Weakly supervised object detection (WSOD) has attracted extensive research attention due
to its great flexibility of exploiting large-scale dataset with only image-level annotations for …

Weakly Supervised Object Localization (WSOL) aims to localize objects with only image-
level labels, which has better scalability and practicability than fully supervised methods in …

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 …

Labeling datasets for supervised object detection is a dull and time-consuming task. Errors
can be easily introduced during annotation and overlooked during review, yielding …