Today’s paper: Pyramidal Convolution by Duta et al. This is the third paper of the new series Deep Learning Papers visualized and it’s about using convolutions in a pyramidal style to capture information of different magnifications from an image. The authors show how a pyramidal convolution can be constructed and apply it to several problems in the visual domain. What’s really interesting is that the number of parameters can be kept the same while performance tends to improve.

Today’s paper: End-to-End object detection with transformers by Carion et al. This is the second paper of the new series Deep Learning Papers visualized and it’s about using a transformer approach (the current state of the art in the domain of speech) to the domain of vision. More specifically, the paper is concerned with object detection and here is the link to the paper of Carion et al. on arxiv.