Object detection is a computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos.
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The standard cross entropy loss for classification is independent of localization task and drives all the positive examples to learn as high classification score as possible regardless of localization accuracy during training.
However, these methods require fully annotated object bounding boxes for training, which are incredibly hard to scale up due to the high annotation cost.
Nowadays, Deep learning techniques show dramatic performance on computer vision area, and they even outperform human.
In this paper, to facilitate the research of person ReID in aerial imagery, we collect a large scale airborne person ReID dataset named as Person ReID for Aerial Imagery (PRAI-1581), which consists of 39, 461 images of 1581 person identities.
We present Matrix Nets (xNets), a new deep architecture for object detection.
Specifically, we present an effective video saliency detector that consists of a spatial refinement network and a spatiotemporal module.
Additionally, we evaluated object detection methods on a novel data set of 17 completely annotated cytology whole slide images (WSI) containing 78, 047 hemosiderophages.
Run sequentially, these GANs allow the generation of synthetic remote sensing imagery complete with segmentation labels.
Weakly supervised object detection (WSOD), where a detector is trained with only image-level annotations, is attracting more and more attention.
#2 best model for Weakly Supervised Object Detection on PASCAL VOC 2012