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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Lin et al (2017) Focal Loss for Dense Object Detection
This is the paper proposed RetinaNet and also the focal loss function to better train object detection models. Object detection models are in two camps, there are two-stage proposal-driven models such as R-CNN, and one-stage detector such that YOLO and SSD. The paper claimed that the prior result on one-stage...
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Cai & Vasconcelos (2018) Cascade R-CNN: Delving into High Quality Object Detection
This paper proposed the cascaded architecture to improve the bounding box
quality in object detection. It is to improve the Faster R-CNN framework, which
is run in two stages.
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Lin et al (2017) Feature Pyramid Networks for Object Detection
This paper proposed feature pyramid network to find scale invariant object detection, i.e., a model that can detect objects of different scales. One way to tackle scale invariant problem is to form an image pyramid of different scale and process each with the same model. This is a brute-force approach....
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He et al (2016) Deep Residual Learning for Image Recognition
This is the ResNet paper. Not only this proposed the shortcut connection
architecture, but also give the architecture for image classification and
object detection.
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