538 citations · 2.3k across the 54 of their papers we have counts for
6 papers · 1 filter
Where are the Masks: Instance Segmentation with Image-level Supervision
Issam H. Laradji, David Vazquez, Mark Schmidt
A major obstacle in instance segmentation is that existing methods often need many per-pixel labels in order to be effective. These labels require large human effort and for certai…
Instance Segmentation with Point Supervision
Issam H. Laradji, Negar Rostamzadeh, Pedro O. Pinheiro +2
Instance segmentation methods often require costly per-pixel labels. We propose a method that only requires point-level annotations. During training, the model only has access to a…
Where are the Blobs: Counting by Localization with Point Supervision
Issam H. Laradji, Negar Rostamzadeh, Pedro O. Pinheiro +2
Object counting is an important task in computer vision due to its growing demand in applications such as surveillance, traffic monitoring, and counting everyday objects. State-of-…
Fast Patch-based Style Transfer of Arbitrary Style
Tian Qi Chen, Mark Schmidt
Artistic style transfer is an image synthesis problem where the content of an image is reproduced with the style of another. Recent works show that a visually appealing style trans…
Play and Learn: Using Video Games to Train Computer Vision Models
Alireza Shafaei, James J. Little, Mark Schmidt
Video games are a compelling source of annotated data as they can readily provide fine-grained groundtruth for diverse tasks. However, it is not clear whether the synthetically gen…
Generalized Fast Approximate Energy Minimization via Graph Cuts: Alpha-Expansion Beta-Shrink Moves
Mark Schmidt, Karteek Alahari
We present alpha-expansion beta-shrink moves, a simple generalization of the widely-used alpha-beta swap and alpha-expansion algorithms for approximate energy minimization. We show…