arxiv link key points introduces transformer into the domain of object detection compared to existing approaches, no need for anchor priors and post processing such as NMS to handle with multiple box predictions matching to single gt. this is possible Read more…
when using cairo with python, the text_extent function call is powerful because it returns coordinate information of the text the user wants to print. However, the information depends on the Context which will be used when calling this function and Read more…
https://docs.python.org/3/reference/expressions.html#operator-precedence
reference: https://stackoverflow.com/questions/46551955/python-3-csv-utf-8-encoding do this at the start of csv writer to ad BOM
nice summary: https://towardsdatascience.com/what-is-teacher-forcing-3da6217fed1c
paper link submitted in 2016 I’m only interested in the stacked hourglass architecture, not about pose segmentation performance. So the points listed below are only related to stacked hourglass architecture. “encoding-decoding” or “conv-deconv” structure is already introduced. This paper goes Read more…
The three all seem to have “downsampling and then upsampling” idea at the core. But what are the differences? Which one is the correct one to coin when referencing the “downsampling + upsampling” idea? Fully Convolutional Network(FCN) submitted: 2014.11.14https://arxiv.org/pdf/1411.4038.pdf remove Read more…
arxiv link key points attempts to train classifier with weakly annotated data from flicker. Thus, this this paper is about “weakly supervised” training. the data used is scraped from flicker where the scrapped data includes hash keywords and descriptions. The Read more…