3 citations · 3 across the 6 of their papers we have counts for
6 papers
A step towards understanding why classification helps regression
Silvia L. Pintea, Yancong Lin, Jouke Dijkstra +1
A number of computer vision deep regression approaches report improved results when adding a classification loss to the regression loss. Here, we explore why this is useful in prac…
Deep vanishing point detection: Geometric priors make dataset variations vanish
Yancong Lin, Ruben Wiersma, Silvia L. Pintea +3
Deep learning has improved vanishing point detection in images. Yet, deep networks require expensive annotated datasets trained on costly hardware and do not generalize to even sli…
NeRD++: Improved 3D-mirror symmetry learning from a single image
Yancong Lin, Silvia-Laura Pintea, Jan van Gemert
Many objects are naturally symmetric, and this symmetry can be exploited to infer unseen 3D properties from a single 2D image. Recently, NeRD is proposed for accurate 3D mirror pla…
Investigating transformers in the decomposition of polygonal shapes as point collections
Andrea Alfieri, Yancong Lin, Jan C. van Gemert
Transformers can generate predictions in two approaches: 1. auto-regressively by conditioning each sequence element on the previous ones, or 2. directly produce an output sequences…
Semi-supervised lane detection with Deep Hough Transform
Yancong Lin, Silvia-Laura Pintea, Jan van Gemert
Current work on lane detection relies on large manually annotated datasets. We reduce the dependency on annotations by leveraging massive cheaply available unlabelled data. We prop…
Deep Hough-Transform Line Priors
Yancong Lin, Silvia L. Pintea, Jan C. van Gemert
Classical work on line segment detection is knowledge-based; it uses carefully designed geometric priors using either image gradients, pixel groupings, or Hough transform variants.…