Prokhorenkova et al (2018) CatBoost: Unbiased boosting with categorical features
CatBoost is a library for random forest. This paper describes the key feature behind it.
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Redmon et al (2016) You Only Look Once: Unified, Real-time Object Detection
This is the paper to propose YOLOv1 network, which reframed object detection as a regression problem. It is a single convolutional network that simultaneously predicts multiple bounding boxes and class probabilities for those boxes. It is to compare against R-CNN but faster and can see the entire image at once....
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Liu et al (2016) SSD: Single Shot MultiBox Detector
This paper distinct from previous work in the sense that the older approach of object detection first hypothesize bounding boxes, resample features for each box, then apply a classifier. This paper proposed a network that does not resample for bounding box hypotheses but equally accurate. It can do high speed...
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Wang et al (2020) HRNet
Classification networks such as AlexNet, VGGNet, GoogLeNet, ResNet are all reducing spatial size and produce a low-resolution representation. High-resolution representation is produced in U-net, for example, using dilated convolution and upsampling. This paper proposes HRNet that maintain high-resolution representation through the whole process. It starts from high resolution convolution stream...
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Wei et al (2016) Convolutional Pose Machine
This paper proposes Convolutional Pose Machines (CPMs), which is a computer vision deep learning model to identify human poses in the form of keypoints. The output of the model are 2D belief maps, i.e., a heatmap of the predicted probability of the location of a keypoint. The architecture of the...
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