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Focal Loss for dense object detection
The dataset used in this paper is the Focal Loss for dense object detection. -
Faster R-CNN object detector
The dataset used in this paper is the Faster R-CNN object detector. -
Microsoft COCO 2014 dataset
The dataset used in this paper is the Microsoft COCO 2014 dataset. -
Faster R-CNN and RetinaNet object detectors
The dataset used in this paper is the Faster R-CNN and RetinaNet object detectors. -
Single Shot MultiBox Detector
Single Shot MultiBox Detector. -
VisDrone'18 dataset for object detection
The VisDrone dataset is a large-scale dataset for object detection in aerial images. -
Oxford Flower-102
Fine-grained image classification, which aims to recognize subordinate level categories, has emerged as a popular research area in the computer vision community. -
Content-Aware Convolutional Neural Networks
Convolutional Neural Networks (CNNs) have achieved great success due to the powerful feature learning ability of convolution layers. Specifically, the standard convolution... -
Attacking Object Detector Using A Universal Targeted Label-Switch Patch
A universal targeted label-switch patch that changes an OD's detection of car on which it is applied, from the correct target class (car) to a bus. -
Learning generative visual models from few training examples
Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories -
PASCAL VOC 2007/12
Few-shot Object Detection dataset -
Alibaba Tianchi competition: Alibaba-Tsinghua Adversarial Challenge on Object...
The dataset used in the paper is the Alibaba Tianchi competition: Alibaba-Tsinghua Adversarial Challenge on Object Detection. -
DualConv: Dual Convolutional Kernels for Lightweight Deep Neural Networks
The proposed DualConv is used to replace the standard convolution in VGG-16 and ResNet-50 to perform image classification experiments on CIFAR-10, CIFAR-100, and ImageNet datasets. -
ImageNet Object Detection Dataset
The ImageNet object detection dataset, which contains a sufficiently large number of images and object categories to reach a conclusion.